The SINTER study: A Recall-by-Genotype design with multidimensional musculoskeletal phenotyping across internal-medicine outpatient clinics

The SINTER study: A Recall-by-Genotype design with multidimensional musculoskeletal phenotyping across internal-medicine outpatient clinics | 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 Research Article The SINTER study: A Recall-by-Genotype design with multidimensional musculoskeletal phenotyping across internal-medicine outpatient clinics Fjorda Koromani, Kirsty Huininga, Siwen Li, Katerina Trajanoska, and 21 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8238983/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Bone mineral density (BMD) measured by DXA remains the clinical standard for diagnosing osteoporosis, but fails to capture the heterogeneity of skeletal fragility, particularly in patients with chronic diseases. We hereby describe the design of the EraSmus medIcal CeNTer skEletal fRagility (SINTER) Study, which integrates polygenic scores for BMD (PGS BMD ) with a multidimensional phenotyping approach in diverse outpatient clinics, as a methodological framework for musculoskeletal research. The study aims at assessing the additional value of combining genetic information with comprehensive musculoskeletal phenotyping to better elucidate the mechanisms of skeletal fragility in chronic conditions. SINTER is an observational, cross-sectional Recall-by-Genotype ( RbG ) study drawn in two stages: (1) genotyping 5,650 patients from nine internal medicine clinics; (2) recalling 1,500 patients from the extremes of the PGS BMD distribution for multidimensional musculoskeletal phenotyping together with 400 patients with a rare condition (mastocytosis). Phenotyping includes imaging (DXA, EOSEdge, pQCT), macro- and tissue-level skeletal properties (ultrasound, reference point indentation), functional assessments (mechanography, handgrip), lifestyle and systemic exposures (diet, physical activity), and patient perceptions obtained via in-depth interview and questionnaires. The study design is unique in combining a RbG framework with multidimensional phenotyping in outpatient-clinics, enabling systematic evaluation of genetic and disease-specific contributors to skeletal fragility. SINTER establishes a methodological template for RbG designs in clinical musculoskeletal research while helping elucidate the mechanisms of skeletal fragility. recall by genotype polygenic score skeletal fragility comorbidity multidimensional phenotyping Figures Figure 1 Figure 2 Layman Summary Osteoporosis is usually diagnosed with a bone scan that measures how dense or strong the bones are. However, some people, especially those with long-term illnesses, still have fragile bones even when their scan does not show bone fragility. The SINTER study at Erasmus MC investigates why. It combines information from a person's genes, bones, muscles, and lifestyle to find out how these factors work together. About 5,650 patients will give a small blood sample for genetic testing, and 1,900 of them will be invited for detailed bone and muscle tests, activity questionnaires, and interviews. The goal is to improve care for fragile bones. INTRODUCTION Skeletal fragility is a multifactorial condition characterized by reduced bone strength and increased risk of fracture. Fragility fractures are the consequence of genetic, environmental, and pathological factors that disrupt bone remodeling, alter matrix composition, or impair mineralisation. Fractures contribute substantially to systemic morbidity, mortality, and increased healthcare costs ( 1 ). Despite the use of dual-energy X-ray absorptiometry (DXA) in the diagnosis of osteoporosis more than half of fragility fractures occur in individuals with bone mineral density (BMD) T-scores above the osteoporotic threshold ( 2 – 4 ) and risk prediction tools like FRAX often underestimate risk in people with comorbidities ( 5 , 6 ). These mismatches highlight that bone strength depends not only on mass but also on geometry, microarchitecture, and material properties which standard densitometry cannot fully capture ( 7 , 8 ). Even when treatments are prescribed, fracture risk is reduced by only 25–50% ( 9 ), and adherence remains low ( 10 ). Skeletal fragility develops across diverse clinical contexts being influenced by comorbidities, lifestyle, and systemic factors ( 11 ). Interactions between these factors complicate risk assessment and underscore the need for study designs that capture heterogeneity in causes, risk profiles, and manifestations. Advances in genetics have enabled the development of polygenic scores (PGS) that summarise inherited liability. Recall-by-Genotype ( RbG ) designs use PGS to select participants along a genetic gradient for further investigation ( 12 , 13 ). Here, we describe the objectives and design of the EraSmus medIcal CeNTer skEletal fRagility (SINTER) Study; acronym chosen as ossification resembles “sintering”- creation of a solid yet dynamic structure through gradual mineral accumulation and hardening. Conceptual framework and rationale Heterogeneity in skeletal fragility The diagnosis of osteoporosis is used operationally to decide “who to treat” procuring a clean definition of subjects at risk of fracture. Nevertheless, fragility fractures in the presence of osteoporosis do not frequently occur in isolation from other disease conditions. Osteoporosis, like other age-related diseases, predominantly occurs in late adulthood; a life stage when changes in bone, muscle, and metabolism often co-occur. Therefore, in research and clinical practice, it is difficult to discern whether the heterogeneous pathways leading to skeletal fragility reflect intrinsic age-related changes in bone structure and material properties, or whether they are mainly driven by comorbidity processes or other risk factors. This complexity highlights the need for study designs that explicitly account for heterogeneity, rather than assuming a uniform mechanistic pathway leading to skeletal fragility. Why multidimensional phenotyping? Traditional reliance on DXA-derived BMD fails to capture the full spectrum of skeletal fragility susceptibility ( 14 , 15 ). Skeletal fragility is determined by bone quantity, quality, and the systemic environment in which bone is embedded. Muscle strength, neuromuscular coordination, diet, physical activity, and other exposures interact with intrinsic bone biology to shape overall fracture risk ( 16 , 17 ). A multidimensional phenotyping approach integrates these factors, providing a comprehensive view that reflects the true complexity of skeletal fragility. By moving beyond single measurements, this approach offers a reproducible framework to understand why skeletal fragility arises in diverse patients and how different pathways converge and lead to fracture. Why Recall-by-Genotype? Genetic discoveries have identified hundreds of loci associated with BMD and fracture ( 18 ). PGS summarizes this information into measures of inherited liability, enabling stratification of individuals according to polygenic predisposition for a given trait ( 19 ). RbG designs take this further by selecting participants at the extremes of a genetic distribution for detailed investigation. Compared with standard case-control or cohort studies, RbG can reduce confounding, improve causal inference, and increase statistical efficiency when in-depth phenotyping is expensive or logistically demanding ( 12 , 20 ). Beyond this, RbG can be applied to explore biological mechanisms, to identify disease subgroups with distinct genetic architectures, to evaluate gene-environment interactions, and to enrich (clinical) trial populations for individuals most likely to exhibit measurable differences in response to exposures such as medical intervention ( 21 ). RbG therefore offers not only a way to test whether polygenic predisposition infuences measurable skeletal traits across comorbidity settings, but also a methodological framework to study heterogeneity, uncover causal pathways, and design more efficient translational studies. Positioning of RbG among study designs Traditional population-based cohort studies provide a broader disease spectrum and longer follow-up than clinical studies. Case-control studies offer efficiency but are prone to selection bias, while randomized trials provide causal inference but are rarely feasible for mechanistic questions or large-scale phenotyping ( 22 ). Traditionally, RbG has been applied to rare variant carriers of pathogenic mutations in monogenic diseases, particularly in mechanistic studies and clinical trials. The approach used in SINTER represents an extension of this principle, i.e., leveraging polygenic scores based on common variants to enable the study of a complex trait and investigating its relationship with other diseases. Study Aim The SINTER study applies an RbG approach complemented with multidimensional musculoskeletal phenotyping aimed at gaining a better understanding of biological mechanisms and disentangling the heterogeneity underlying skeletal fragility. By leveraging genetic stratification, SINTER seeks to enhance biological signals (by reducing confounding), supporting causal inference, and elucidating the polygenic contribution to fragility fractures. MATERIAL AND METHODS Study design and setting The SINTER study approved by the Medical Ethics Committee of Erasmus MC MEC-2024-0633 is an observational, cross-sectional investigation using an RbG framework. Although RbG contributes to balancing comorbidities between groups, in some instances multimorbidity may not fully be randomized; by considering multimorbidity systematically in the study design, SINTER can evaluate both the confounding and modifying effects on skeletal fragility. The study is embedded in routine clinical care at Erasmus MC, Rotterdam, the Netherlands, and is conducted within the Department of Internal Medicine. Patients are recruited from nine outpatient clinics (see: Target population section), representing a wide spectrum of chronic conditions. Participants and recruitment In the Netherlands, patients treated at the specialized academic setting of university hospitals, such as Erasmus MC, differ markedly from those in primary care settings ( 23 ). They comprise complex cases with rare (genetic) diseases and/or atypical presentations of common chronic conditions. Patients are referred because conventional diagnostics have not identified a cause, and/or treatments have been either ineffective or require specialized therapies not prescribed in general hospitals. This clinical enrichment provides an opportunity to examine skeletal fragility in groups that are seldom represented in population-based cohorts. SINTER target population Participants include adults (≥ 18 years) attending one of nine outpatient clinics: bone center, diabetes clinic, healthy weight, nephrology, vascular medicine, thyroid center, geriatrics, neuroendocrine tumours and mastocytosis. These clinics, while not a comprehensive selection, do represent a heterogeneous patient population at risk of skeletal fragility, arising from metabolic disorders, endocrine dysfunction, and/or chronic inflammatory conditions. In addition, the mastocytosis clinic comprises the inclusion of a sporadic, somatic clonal disorder with skeletal presentation, which differs etiologically from the other hereditary mono- or polygenic conditions. This patient base ensures that SINTER captures skeletal fragility in clinical contexts where prior evidence indicates increased fracture susceptibility, while ensuring feasibility by recruiting patients within the internal medicine setting of one department. Recruitment strategy and consent procedures Recruitment follows a two-stage process aligned with the RbG design (Fig. 1 ). Stage 1 (genotyping pool): Patients attending these clinics will be invited to participate and be genotyped as a selection tool for future investigations. Multiple complementary approaches are employed to maximize reach: During consultation, treating physicians inform patients about the study, and their willingness to be contacted is recorded directly in the electronic health record (HiX). Posters displayed at outpatient waiting areas to allow patients to self-refer by contacting the study team. The Patient Information Folder (PIF) is included with appointment letters. All patients are given the option to return signed consent by post or electronically via ValidSign ( https://www.validsign.eu ). When written informed consent is obtained either in person or electronically, an additional 20 ml of blood is collected during the next routine blood draw and bio-banked for genomic analysis. Stage 2 ( RbG ): Once genotyping and PGS BMD calculation are complete, individuals in the upper and lower quartiles of the PGS distribution become eligible for recall and are invited for detailed musculoskeletal phenotyping. Inclusion and exclusion criteria Eligibility criteria are tailored to each study stage to ensure safety, data quality, and feasibility. Stage 1 (genotyping): Adults ≥ 18 years from one of the nine clinics, able to provide informed consent and a blood sample, and willing to be re-contacted. Exclusions are terminal illness, palliative care, pregnancy, refusal of re-contact, or legal incapacity. Stage 2 (recall and phenotyping): participants with a PGS BMD score in the top or bottom quartile who consented to be recalled. General exclusions are severe obesity (BMI > 40 kg/m² or > 140 kg), unstable cardiovascular disease, active malignancy, severe gastrointestinal, renal, or hepatic disease, systemic infection, or any condition deemed unsafe. For reference point indentation, additional exclusions include local tibial pathology (e.g. oedema, infection, fracture, tumor, osteomyelitis) or allergy to lidocaine. Multidimensional phenotyping A central feature of the SINTER study is its multidimensional phenotyping approach, which captures skeletal fragility susceptibility across complementary musculoskeletal domains. Participants recalled in Stage 2 undergo a standardized set of assessments organised into imaging, tissue-level, functional, systemic, and patient-reported domains (Fig. 2 ). Imaging assessments Dual-energy X-ray absorptiometry (DXA): performed at the lumbar spine and hip, with whole-body scans obtained for body composition analysis using a GE-Lunar iDXA device. This enables assessment of both traditional BMD metrics and derived measures such as lean and fat mass, relevant for understanding sarcopenia and obesity-related effects on bone. EOS imaging: Acquisition of full-body radiographs in an upright, weight-bearing position, will be performed using an EOSEdge system employing photo-counting technology and equipped with a dual-energy detector. Three-dimensional reconstructions are made possible through simultaneous acquisition using two perpendicular X-ray beams that provide measurements of vertebral morphology, sagittal alignment, pelvic incidence (parameter used to help determine the sagittal balance of the spine), and overall spinal geometry ( 24 ). Peripheral quantitative computed tomography (pQCT) of the tibia and radius: cross-sectional images to quantify volumetric BMD, cortical and trabecular bone density, cortical thickness, and geometric indices of bone strength using an STRATEC system. pQCT measures and partitions structural parameters of skeletal fragility not captured by DXA, which are informative for chronic diseases that preferentially affect cortical or trabecular compartments. Bone Tissue assessments Heel ultrasound: measurements of speed of sound (SOS) and broadband ultrasound attenuation (BUA) lack the precision and standardization of DXA when used for the estimation of BMD; but they can provide additional indirect insight about bone quality properties, including microarchitecture and elasticity, bone stiffness and structural strength. Further, their use in large scale biobanks ( 25 ) warrants their use for replication and translational efforts. Reference point indentation (RPI): Bone material strength index (BMSi) is assessed at the tibia using a BoneScore Active Life Scientific system. This minimally invasive technique measures the mechanical resistance of bone tissue to indentation and reflects material-level properties of bone that are not accessible by imaging alone ( 26 ). Given the evidence that alterations in tissue material properties contribute to fracture risk ( 27 ), RPI provides an important complementary dimension to skeletal phenotyping. Functional assessments Mechanography: Neuromuscular function is evaluated using a Leonardo force platform to record parameters during jump tests. For participants unable to perform jumps, a sit-to-stand chair rise test is conducted, assessing lower-limb strength and endurance. These measures address the contribution of muscle performance and fall risk to skeletal fragility ( 28 ). Handgrip strength (HGS): HGS is measured with a Leonardo handheld dynamometer, with participants seated and the non-dominant arm flexed at 90°. Grip strength is recorded in three repeated trials, with short rests to ensure reproducibility. Work is registered after asking participants to grip continuously for as long as they can hold. HGS and work are simple, validated biomarkers of global muscle function. Systemic exposures Lifestyle assessment: Physical activity is evaluated with the International Physical Activity Questionnaire (IPAQ) ( 29 ), which quantifies duration and frequency of physical activity domains including walking, cycling, sport, and occupational activity. Diet is assessed using a validated Food Frequency Questionnaire (FFQ) ( 30 ), capturing food type, preparation method, portion size, and frequency of consumption. Macronutrient and micronutrient intake, including calcium and vitamin D, are calculated using the Dutch Food Consumption Database ( 31 ). Polygenic Scores (PGS) and Recall by Genotype (RbG) All participants recruited in Stage 1 provide a blood sample that is bio banked for genomic analysis. DNA is extracted and genotyped using the Illumina Global Screening Array v4 (GSA v4),which provides genome-wide coverage (≈ 650K backbone markers) with a design that is enriched for variants curated from ClinVar, NHGRI-EBI GWAS catalogs, PharmGKB, and ExAC, thus optimizing content for traits relevant to metabolism, bone, and other complex traits ( 32 ). Quality control and imputation Genotyping data undergo standard quality control (QC) procedures, including call rate thresholds, Hardy–Weinberg equilibrium testing, sex concordance checks, and evaluation of heterozygosity outliers. Samples failing QC are excluded, as are variants with low call rates or high missingness. Following QC, imputation is performed using the Haplotype Reference Consortium (HRC) ( 33 ) and TOPMED ( 34 ) panels, providing dense genome-wide coverage and enabling accurate calculation of PGS. Construction of PGS BMD PGS BMD will be derived from the largest available genome-wide association study (GWAS) summary statistics identifying loci associated with BMD in populations of European background. The specific BMD phenotype (e.g. femoral neck, lumbar spine, heel, or total body) used for the PGS will be determined based on the most comprehensive and up-to-date GWAS available at the time of score construction, to maximize power and relevance. The PGS will be constructed as a weighted sum of effect alleles across associated loci, with weights corresponding to published effect sizes using PRSice2 ( 35 ). Scores will be standardized within the genotyped cohort to enable comparison across individuals and strata. Portability across ancestral backgrounds will be monitored to ensure sampling is not biased at the extremes of the PGS distribution with under- or over-representation of specific groups of individuals ( 34 , 36 ). Stratification and recall Individuals across eight clinics are stratified into quartiles of the PGS BMD distribution. Those in the highest and lowest quartiles (25% tails) are eligible for recall to Stage 2 seeking to recruit those at the top and lowest 15% tails. Moreover, all patients of the mastocytosis clinic will be invited to Stage 2 without including them in the recall pool. The RbG strategy ensures efficiency and power by enriching the study sample with individuals at genetic extremes, while maintaining clinical diversity. Patient perceptions and perspectives A pilot study of 30 participants, (every 50th patient systematically selected from each clinic), will record via perception questionnaires and semi-structured interviews, their knowledge, attitudes, and beliefs regarding skeletal health, genetics, and research participation. Items address privacy concerns, willingness to engage in genetic research, and understanding of skeletal fragility. Such insights will guide the design of future recruitment strategies and warrant patient-centered care. Outcomes, feasibility, and rigor The SINTER study is designed with a dual methodological aim: to evaluate the feasibility of implementing an RbG framework in a heterogeneous outpatient setting and to test whether multidimensional phenotyping in a RbG framework can reveal skeletal differences across genetic and clinical strata. Outcomes are therefore structured into primary methodological outcomes and secondary analytic outcomes. Primary methodological outcomes The first set of outcomes concerns the feasibility and reproducibility of the study design itself: Recruitment and recall rates: the proportion of eligible patients consenting to Stage 1 genotyping and subsequently agreeing to Stage 2 recall. Protocol adherence: the extent to which participants complete the full multidimensional phenotyping examinations. Data completeness: proportion of datasets with missing or unusable information across domains. These methodological outcomes will determine whether RbG with multidimensional phenotyping can be executed efficiently and reliably in a busy clinical environment. Secondary analytic outcomes The second set of outcomes evaluates the scientific yield of the design: PGS-stratified skeletal phenotypes: differences in aBMD, volumetric density, cortical/trabecular structure, BMSi, mechanography, and alignment indices between the extremes of the PGS BMD distribution. Exploratory associations: integration of lifestyle exposures with skeletal outcomes, and their potential modification by genetic risk. Statistical plan and power Analytical framework The primary analytical goal is to test whether participants at the extremes of the PGS BMD distribution constitute well-defined contrast groups that differ systematically in skeletal parameters captured by the comprehensive phenotypic assessment. Analyses will compare high-PGS and low-PGS groups using linear regression for continuous outcomes (e.g., aBMD, cortical thickness, BMSi). Sample size justification The required size of the genotyped pool depends on the correlation (R²) between PGS and measured BMD, and between BMD and fracture risk. Based on published evidence, we assume: R² between measured BMD and fracture risk ≈ 0.10. R² between PGS BMD and measured BMD ≈ 0.06. Under these assumptions, a two-tailed α = 0.05 and expected recall rate of 57% provide 80% power to detect differences of 0.15 standard deviations (SD) in BMD-related phenotypes between PGS extremes. With 5,250 successfully genotyped participants, 2,625 individuals are expected to fall into the top and bottom quartiles, of whom 1,500 participate in Stage 2 recall, yielding 750 participants per PGS-defined group. Detectable effects With the above design, the RbG study is powered to detect effect sizes of differences ≥ 0.15 SD in skeletal phenotypes (e.g., cortical thickness, trabecular density) for continuous outcomes; and odds ratios of 1.8 for fracture prevalence when comparing PGS extremes, under the assumption of a 5% exposure probability in high-BMD controls for binary outcomes. Proof of concept of the RbG approach The Rotterdam Study, established in 1990 in the Ommoord district of Rotterdam, The Netherlands, is a prospective, population-based cohort study designed to examine the incidence, determinants, and prognosis of chronic diseases in middle-aged and older adults which to date has evaluated close to 18,000 participants( 37 ). In a selection of 5331 participants from RSI, RSII, and RSIII with genotype and phenotype information, a weighted PGS was constructed for total body BMD using 81 SNPs ( 38 ). Disease biomarkers specific to the conditions under study, together with sex and age were compared between the 25% lower (n = 666) and higher tail (n = 666) of the BMD PGS distribution. Participants in the lower tail had lower femoral-neck BMD and higher fracture prevalence than those at the upper tail, while age, sex, and all other comorbidity factors remained balanced across groups (Table 1 ). These findings, which are consistent with the Mendelian randomization framework under which PGSs are derived, illustrate that polygenic extremes can preserve unconfounded (randomized) and well-defined contrasts in skeletal outcomes, even in the presence of heterogeneous sampling. This supports the utility and optimal design of RbG approaches for deeper phenotyping in clinical studies ( 36 ). DISCUSSION Expected utility SINTER seeks to demonstrate the feasibility of integrating RbG with multidimensional phenotyping in musculoskeletal research. Such methodology opens paths for a “genetics-first” approach in musculoskeletal research, where genomic information is used to complement established clinical assessments and to refine hypotheses for future translational or interventional studies. By recalling individuals from the PGS BMD extremes, an unconfounded and power-optimized study setting is created, which allows testing whether genetic predisposition translates into measurable differences in skeletal structure, bone material properties, and neuromuscular function that are not readily identified by comparing groups differing in measured BMD alone. Further, recruitment of patients with underlying chronic diseases will provide valuable insights into the interaction between chronic conditions and genetic predisposition, potentially identifying disease-specific skeletal signatures and clarifying why conventional diagnostic and treatment tools often underperform in these groups. From another perspective, the resulting genotyped pool will provide a long-term resource for replication, Mendelian randomization, and expansion to other phenotypes; findings may inform future strategies for genetics-informed risk stratification and targeted phenotyping. Ultimately, findings from SINTER could help define the pathophysiology of skeletal fragility and support the end goal of patient stratification tailoring treatments based on genetic susceptibility and disease risk. Strengths and limitations The SINTER study has several methodological strengths. It is the first RbG design implemented in musculoskeletal outpatient clinics, integrating genetic stratification directly into clinical workflows of patients rather than confining it to a research-based framework. The study employs an extensive multidimensional phenotyping approach, encompassing imaging, tissue-level analysis, functional, systemic, and patient-reported outcomes and thus enabling a comprehensive characterization of skeletal fragility that goes beyond the traditional BMD paradigm. Reproducibility is ensured through standardized procedures and pre-specified analyses. However, there are limitations. The study is conducted at a single tertiary center which may limit generalizability. Furthermore, sample size within comorbidity subgroups may be modest, and the PGS BMD is derived from European-ancestry GWAS, potentially reducing utility in underrepresented ancestries. Replication in larger, longitudinal, and more diverse settings will therefore be needed. Genetic and phenotypic data are linked through secure, de-identified databases under GDPR-compliant governance. Declarations Conflicts of interest: F.K, K.H, S.L, K.T, K.B., M.B., L.C, P.v.D., M.v.H., M.H., J.R.v.L., M.M., J.O., B.O., R.P., F.M-R., J.v.R., A.U., W.V., F.R. have no conflict of interests. E.v.V reports honoraria from USB Pharma, Kyowa Kirin and Theromex and Amgen not related to this work. M.C.Z reports honoraria for lectures or consulting from USB Pharma, Kyowa Kirin and Theromex and Amgen not related to this work. E.F.C.v.R reports honoraria from Dutch Obesity Academy, E-wise and Medscape/WebMD not related to this work. L.C has received funding for investigator-initiated research from Abbott Diabetes Care not related to this work. Funding: Setup and conduction of the SINTER Study together with KH, FK, SL, KT and FR are funded by the European Research Council (ERC) under Horizon 2020 research and innovation programme (Grant agreement No. 101021500 – LEGENDARE Advance Grant). Author Contributions: Fjorda Koromani (Conceptualization, Data curation, Investigation, Methodology, Visualization, Writing—original draft, Supervision, Writing—review & editing), Kirsty Huininga (Conceptualization, Data curation, Investigation, Methodology, Visualization, Writing—original draft, Writing—review & editing), Siwen Li (Writing – review & editing), Katerina Trajanoska (Writing – review & editing), Kirsten Berk (Writing – review & editing), References Johnell O, Kanis JA. 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Ikram MA, Kieboom BCT, Brouwer WP, Brusselle G, Chaker L, Ghanbari M, et al. The Rotterdam Study. Design update and major findings between 2020 and 2024. Eur J Epidemiol. 2024;39(2):183–206. Medina-Gomez C, Kemp JP, Trajanoska K, Luan J, Chesi A, Ahluwalia TS, et al. Life-Course Genome-wide Association Study Meta-analysis of Total Body BMD and Assessment of Age-Specific Effects. Am J Hum Genet. 2018;102(1):88–102. Table 1 Table 1. Characteristics of the study population compared between low and high BMD wPGS. As expected, the measured FN-BMD is lower in the low PGS BMD compared to the high PGS BMD group whereas fracture prevalence is higher in the low PGS BMD compared to the high PGS BMD group. All other covariates do not differ between groups, thus showing the randomization properties of stratifying PGS. Low BMD wPGS n=666 High BMD wPGS n=666 P-value wPGS BMD (SD) 4.67 (0.13) 5.72 (0.14) <0.001 FN-BMD (SD) 0.83 (0.13) 0.92 (0.14) 10% (%) 173 (26.0) 164 (24.6) 0.6 T2D (%) 87 (13.1) 99 (14.9) 0.3 TSH >3.85 mU/L (%) 90 (13.5) 104 (15.6) 0.3 eGFR 31 kg/m2 (%) 92 (13.8) 99 (14.9) 0.6 Cholesterol >7.5 mmol/L (%) 86 (12.9) 98 (14.7) 0.3 wPGS- weighted polygenic score, BMD- bone mineral density OPFx- prevalent osteoporotic fractures, MOF- major osteoporotic fractures, TSH- thyroid stimulating hormone, eGFR- estimated glomerular filtration rate, BMI- body mass index Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 30 Jan, 2026 Editor invited by journal 08 Dec, 2025 Reviewers invited by journal 03 Dec, 2025 Editor assigned by journal 03 Dec, 2025 First submitted to journal 29 Nov, 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. 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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-8238983","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554714562,"identity":"8cdd96ba-f8de-458d-82cc-c579247101b7","order_by":0,"name":"Fjorda Koromani","email":"","orcid":"","institution":"Erasmus Medical Centre: Erasmus MC","correspondingAuthor":false,"prefix":"","firstName":"Fjorda","middleName":"","lastName":"Koromani","suffix":""},{"id":554714563,"identity":"5c69d967-1436-471a-bc69-47da2e47a01b","order_by":1,"name":"Kirsty 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1","display":"","copyAsset":false,"role":"figure","size":115104,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSINTER study design. Created using Biorender.\u003c/strong\u003e Patients are recruited from nine outpatient clinics within Erasmus MC Internal Medicine: bone center, diabetes clinic, healthy weight, nephrology, vascular medicine, thyroid center, geriatrics, neuroendocrine and mastocytosis. In Stage 1, 5,250 patients from eight clinics undergo DNA collection and genotyping. Polygenic scores (PGS) for BMD are calculated, and individuals in the highest and lowest quartiles (≈25% each) are eligible for recall. In Stage 2, 1,500 participants from these genetic extremes undergo multidimensional deep phenotyping. In parallel, a separate cohort of 400 mastocytosis patients is genotyped and phenotyped in full. This recall-by-genotype design enables unconfounded contrasts across genetic risk strata and facilitates the study of skeletal fragility heterogeneity.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8238983/v1/c1af4d03987b559f10dfa32a.jpg"},{"id":97687301,"identity":"82d01d7e-8e8a-4e69-a80e-9d4462622d9b","added_by":"auto","created_at":"2025-12-08 10:25:31","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127123,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of multidimensional phenotyping in the SINTER study. Created using Biorender.\u003c/strong\u003e\u003cbr\u003e\n Participants undergo a standardized panel of assessments across genetic, imaging, tissue-level, and functional domains. Genotyping of blood samples is used to construct polygenic scores (PGS). Bone structure and composition are assessed by dual-energy X-ray absorptiometry (DXA), EOS Edge imaging, and peripheral quantitative computed tomography (pQCT). Tissue-level properties are measured by reference point indentation. Functional assessments include mechanography (jump and chair rise tests) and handgrip strength.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8238983/v1/892417f9f4faddeca2acb0d5.jpg"},{"id":97902416,"identity":"ac8abc41-e0ae-4462-9a67-7577195c66c3","added_by":"auto","created_at":"2025-12-10 15:52:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1414536,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8238983/v1/3aa66428-d7aa-464c-bf04-5f36f78b946f.pdf"}],"financialInterests":"","formattedTitle":"The SINTER study: A Recall-by-Genotype design with multidimensional musculoskeletal phenotyping across internal-medicine outpatient clinics","fulltext":[{"header":"Layman Summary","content":"\u003cp\u003eOsteoporosis is usually diagnosed with a bone scan that measures how dense or strong the bones are. However, some people, especially those with long-term illnesses, still have fragile bones even when their scan does not show bone fragility. The SINTER study at Erasmus MC investigates why. It combines information from a person\u0026apos;s genes, bones, muscles, and lifestyle to find out how these factors work together. About 5,650 patients will give a small blood sample for genetic testing, and 1,900 of them will be invited for detailed bone and muscle tests, activity questionnaires, and interviews. The goal is to improve care for fragile bones.\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eSkeletal fragility is a multifactorial condition characterized by reduced bone strength and increased risk of fracture. Fragility fractures are the consequence of genetic, environmental, and pathological factors that disrupt bone remodeling, alter matrix composition, or impair mineralisation. Fractures contribute substantially to systemic morbidity, mortality, and increased healthcare costs (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the use of dual-energy X-ray absorptiometry (DXA) in the diagnosis of osteoporosis more than half of fragility fractures occur in individuals with bone mineral density (BMD) T-scores above the osteoporotic threshold (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) and risk prediction tools like FRAX often underestimate risk in people with comorbidities (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). These mismatches highlight that bone strength depends not only on mass but also on geometry, microarchitecture, and material properties which standard densitometry cannot fully capture (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Even when treatments are prescribed, fracture risk is reduced by only 25\u0026ndash;50% (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), and adherence remains low (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSkeletal fragility develops across diverse clinical contexts being influenced by comorbidities, lifestyle, and systemic factors (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Interactions between these factors complicate risk assessment and underscore the need for study designs that capture heterogeneity in causes, risk profiles, and manifestations. Advances in genetics have enabled the development of polygenic scores (PGS) that summarise inherited liability. Recall-by-Genotype (\u003cem\u003eRbG\u003c/em\u003e) designs use PGS to select participants along a genetic gradient for further investigation (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Here, we describe the objectives and design of the \u003cem\u003eEraSmus medIcal CeNTer skEletal fRagility\u003c/em\u003e (SINTER) Study; acronym chosen as ossification resembles \u0026ldquo;sintering\u0026rdquo;- creation of a solid yet dynamic structure through gradual mineral accumulation and hardening.\u003c/p\u003e\n\u003ch3\u003eConceptual framework and rationale\u003c/h3\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eHeterogeneity in skeletal fragility\u003c/h2\u003e\u003cp\u003eThe diagnosis of osteoporosis is used operationally to decide \u0026ldquo;who to treat\u0026rdquo; procuring a clean definition of subjects at risk of fracture. Nevertheless, fragility fractures in the presence of osteoporosis do not frequently occur in isolation from other disease conditions. Osteoporosis, like other age-related diseases, predominantly occurs in late adulthood; a life stage when changes in bone, muscle, and metabolism often co-occur. Therefore, in research and clinical practice, it is difficult to discern whether the heterogeneous pathways leading to skeletal fragility reflect intrinsic age-related changes in bone structure and material properties, or whether they are mainly driven by comorbidity processes or other risk factors. This complexity highlights the need for study designs that explicitly account for heterogeneity, rather than assuming a uniform mechanistic pathway leading to skeletal fragility.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eWhy multidimensional phenotyping?\u003c/h3\u003e\n\u003cp\u003eTraditional reliance on DXA-derived BMD fails to capture the full spectrum of skeletal fragility susceptibility (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Skeletal fragility is determined by bone quantity, quality, and the systemic environment in which bone is embedded. Muscle strength, neuromuscular coordination, diet, physical activity, and other exposures interact with intrinsic bone biology to shape overall fracture risk (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). A multidimensional phenotyping approach integrates these factors, providing a comprehensive view that reflects the true complexity of skeletal fragility. By moving beyond single measurements, this approach offers a reproducible framework to understand why skeletal fragility arises in diverse patients and how different pathways converge and lead to fracture.\u003c/p\u003e\n\u003ch3\u003eWhy Recall-by-Genotype?\u003c/h3\u003e\n\u003cp\u003eGenetic discoveries have identified hundreds of loci associated with BMD and fracture (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). PGS summarizes this information into measures of inherited liability, enabling stratification of individuals according to polygenic predisposition for a given trait (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). \u003cem\u003eRbG\u003c/em\u003e designs take this further by selecting participants at the extremes of a genetic distribution for detailed investigation. Compared with standard case-control or cohort studies, \u003cem\u003eRbG\u003c/em\u003e can reduce confounding, improve causal inference, and increase statistical efficiency when in-depth phenotyping is expensive or logistically demanding (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Beyond this, \u003cem\u003eRbG\u003c/em\u003e can be applied to explore biological mechanisms, to identify disease subgroups with distinct genetic architectures, to evaluate gene-environment interactions, and to enrich (clinical) trial populations for individuals most likely to exhibit measurable differences in response to exposures such as medical intervention (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). \u003cem\u003eRbG\u003c/em\u003e therefore offers not only a way to test whether polygenic predisposition infuences measurable skeletal traits across comorbidity settings, but also a methodological framework to study heterogeneity, uncover causal pathways, and design more efficient translational studies.\u003c/p\u003e\n\u003ch3\u003ePositioning of RbG among study designs\u003c/h3\u003e\n\u003cp\u003eTraditional population-based cohort studies provide a broader disease spectrum and longer follow-up than clinical studies. Case-control studies offer efficiency but are prone to selection bias, while randomized trials provide causal inference but are rarely feasible for mechanistic questions or large-scale phenotyping (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Traditionally, \u003cem\u003eRbG\u003c/em\u003e has been applied to rare variant carriers of pathogenic mutations in monogenic diseases, particularly in mechanistic studies and clinical trials. The approach used in SINTER represents an extension of this principle, i.e., leveraging polygenic scores based on common variants to enable the study of a complex trait and investigating its relationship with other diseases.\u003c/p\u003e\n\u003ch3\u003eStudy Aim\u003c/h3\u003e\n\u003cp\u003eThe SINTER study applies an \u003cem\u003eRbG\u003c/em\u003e approach complemented with multidimensional musculoskeletal phenotyping aimed at gaining a better understanding of biological mechanisms and disentangling the heterogeneity underlying skeletal fragility. By leveraging genetic stratification, SINTER seeks to enhance biological signals (by reducing confounding), supporting causal inference, and elucidating the polygenic contribution to fragility fractures.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and setting\u003c/h2\u003e\u003cp\u003eThe SINTER study approved by the Medical Ethics Committee of Erasmus MC MEC-2024-0633 is an observational, cross-sectional investigation using an \u003cem\u003eRbG\u003c/em\u003e framework. Although \u003cem\u003eRbG\u003c/em\u003e contributes to balancing comorbidities between groups, in some instances multimorbidity may not fully be randomized; by considering multimorbidity systematically in the study design, SINTER can evaluate both the confounding and modifying effects on skeletal fragility. The study is embedded in routine clinical care at Erasmus MC, Rotterdam, the Netherlands, and is conducted within the Department of Internal Medicine. Patients are recruited from nine outpatient clinics (see: Target population section), representing a wide spectrum of chronic conditions.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eParticipants and recruitment\u003c/h3\u003e\n\u003cp\u003eIn the Netherlands, patients treated at the specialized academic setting of university hospitals, such as Erasmus MC, differ markedly from those in primary care settings (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). They comprise complex cases with rare (genetic) diseases and/or atypical presentations of common chronic conditions. Patients are referred because conventional diagnostics have not identified a cause, and/or treatments have been either ineffective or require specialized therapies not prescribed in general hospitals. This clinical enrichment provides an opportunity to examine skeletal fragility in groups that are seldom represented in population-based cohorts.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSINTER target population\u003c/h2\u003e\u003cp\u003eParticipants include adults (\u0026ge;\u0026thinsp;18 years) attending one of nine outpatient clinics: bone center, diabetes clinic, healthy weight, nephrology, vascular medicine, thyroid center, geriatrics, neuroendocrine tumours and mastocytosis. These clinics, while not a comprehensive selection, do represent a heterogeneous patient population at risk of skeletal fragility, arising from metabolic disorders, endocrine dysfunction, and/or chronic inflammatory conditions. In addition, the mastocytosis clinic comprises the inclusion of a sporadic, somatic clonal disorder with skeletal presentation, which differs etiologically from the other hereditary mono- or polygenic conditions. This patient base ensures that SINTER captures skeletal fragility in clinical contexts where prior evidence indicates increased fracture susceptibility, while ensuring feasibility by recruiting patients within the internal medicine setting of one department.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eRecruitment strategy and consent procedures\u003c/h2\u003e\u003cp\u003eRecruitment follows a two-stage process aligned with the \u003cem\u003eRbG\u003c/em\u003e design (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStage 1\u003c/span\u003e (genotyping pool): Patients attending these clinics will be invited to participate and be genotyped as a selection tool for future investigations. Multiple complementary approaches are employed to maximize reach:\u003c/p\u003e\u003cp\u003e\u003col style=\"list-style-type:lower-alpha;\"\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDuring consultation, treating physicians inform patients about the study, and their willingness to be contacted is recorded directly in the electronic health record (HiX).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePosters displayed at outpatient waiting areas to allow patients to self-refer by contacting the study team.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe Patient Information Folder (PIF) is included with appointment letters.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eAll patients are given the option to return signed consent by post or electronically via ValidSign (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.validsign.eu\u003c/span\u003e\u003cspan address=\"https://www.validsign.eu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). When written informed consent is obtained either in person or electronically, an additional 20 ml of blood is collected during the next routine blood draw and bio-banked for genomic analysis.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStage 2\u003c/span\u003e (\u003cem\u003eRbG\u003c/em\u003e): Once genotyping and PGS\u003csub\u003eBMD\u003c/sub\u003e calculation are complete, individuals in the upper and lower quartiles of the PGS distribution become eligible for recall and are invited for detailed musculoskeletal phenotyping.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eInclusion and exclusion criteria\u003c/h2\u003e\u003cp\u003eEligibility criteria are tailored to each study stage to ensure safety, data quality, and feasibility.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStage 1\u003c/em\u003e (genotyping): Adults\u0026thinsp;\u0026ge;\u0026thinsp;18 years from one of the nine clinics, able to provide informed consent and a blood sample, and willing to be re-contacted. Exclusions are terminal illness, palliative care, pregnancy, refusal of re-contact, or legal incapacity.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStage 2\u003c/em\u003e (recall and phenotyping): participants with a PGS\u003csub\u003eBMD\u003c/sub\u003e score in the top or bottom quartile who consented to be recalled. General exclusions are severe obesity (BMI\u0026thinsp;\u0026gt;\u0026thinsp;40 kg/m\u0026sup2; or \u0026gt;\u0026thinsp;140 kg), unstable cardiovascular disease, active malignancy, severe gastrointestinal, renal, or hepatic disease, systemic infection, or any condition deemed unsafe. For reference point indentation, additional exclusions include local tibial pathology (e.g. oedema, infection, fracture, tumor, osteomyelitis) or allergy to lidocaine.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eMultidimensional phenotyping\u003c/h2\u003e\u003cp\u003eA central feature of the SINTER study is its multidimensional phenotyping approach, which captures skeletal fragility susceptibility across complementary musculoskeletal domains. Participants recalled in Stage 2 undergo a standardized set of assessments organised into imaging, tissue-level, functional, systemic, and patient-reported domains (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eImaging assessments\u003c/h2\u003e\u003cp\u003eDual-energy X-ray absorptiometry (DXA): performed at the lumbar spine and hip, with whole-body scans obtained for body composition analysis using a GE-Lunar iDXA device. This enables assessment of both traditional BMD metrics and derived measures such as lean and fat mass, relevant for understanding sarcopenia and obesity-related effects on bone.\u003c/p\u003e\u003cp\u003eEOS imaging: Acquisition of full-body radiographs in an upright, weight-bearing position, will be performed using an EOSEdge system employing photo-counting technology and equipped with a dual-energy detector. Three-dimensional reconstructions are made possible through simultaneous acquisition using two perpendicular X-ray beams that provide measurements of vertebral morphology, sagittal alignment, pelvic incidence (parameter used to help determine the sagittal balance of the spine), and overall spinal geometry (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePeripheral quantitative computed tomography (pQCT) of the tibia and radius: cross-sectional images to quantify volumetric BMD, cortical and trabecular bone density, cortical thickness, and geometric indices of bone strength using an STRATEC system. pQCT measures and partitions structural parameters of skeletal fragility not captured by DXA, which are informative for chronic diseases that preferentially affect cortical or trabecular compartments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eBone Tissue assessments\u003c/h2\u003e\u003cp\u003eHeel ultrasound: measurements of speed of sound (SOS) and broadband ultrasound attenuation (BUA) lack the precision and standardization of DXA when used for the estimation of BMD; but they can provide additional indirect insight about bone quality properties, including microarchitecture and elasticity, bone stiffness and structural strength. Further, their use in large scale biobanks (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) warrants their use for replication and translational efforts.\u003c/p\u003e\u003cp\u003eReference point indentation (RPI): Bone material strength index (BMSi) is assessed at the tibia using a BoneScore Active Life Scientific system. This minimally invasive technique measures the mechanical resistance of bone tissue to indentation and reflects material-level properties of bone that are not accessible by imaging alone (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Given the evidence that alterations in tissue material properties contribute to fracture risk (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), RPI provides an important complementary dimension to skeletal phenotyping.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eFunctional assessments\u003c/h2\u003e\u003cp\u003eMechanography: Neuromuscular function is evaluated using a Leonardo force platform to record parameters during jump tests. For participants unable to perform jumps, a sit-to-stand chair rise test is conducted, assessing lower-limb strength and endurance. These measures address the contribution of muscle performance and fall risk to skeletal fragility (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHandgrip strength (HGS): HGS is measured with a Leonardo handheld dynamometer, with participants seated and the non-dominant arm flexed at 90\u0026deg;. Grip strength is recorded in three repeated trials, with short rests to ensure reproducibility. Work is registered after asking participants to grip continuously for as long as they can hold. HGS and work are simple, validated biomarkers of global muscle function.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eSystemic exposures\u003c/h2\u003e\u003cp\u003eLifestyle assessment: Physical activity is evaluated with the International Physical Activity Questionnaire (IPAQ) (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), which quantifies duration and frequency of physical activity domains including walking, cycling, sport, and occupational activity. Diet is assessed using a validated Food Frequency Questionnaire (FFQ) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), capturing food type, preparation method, portion size, and frequency of consumption. Macronutrient and micronutrient intake, including calcium and vitamin D, are calculated using the Dutch Food Consumption Database (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003ePolygenic Scores (PGS) and Recall by Genotype (RbG)\u003c/h2\u003e\u003cp\u003e All participants recruited in Stage 1 provide a blood sample that is bio banked for genomic analysis. DNA is extracted and genotyped using the Illumina Global Screening Array v4 (GSA v4),which provides genome-wide coverage (\u0026asymp;\u0026thinsp;650K backbone markers) with a design that is enriched for variants curated from ClinVar, NHGRI-EBI GWAS catalogs, PharmGKB, and ExAC, thus optimizing content for traits relevant to metabolism, bone, and other complex traits (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eQuality control and imputation\u003c/h2\u003e\u003cp\u003eGenotyping data undergo standard quality control (QC) procedures, including call rate thresholds, Hardy\u0026ndash;Weinberg equilibrium testing, sex concordance checks, and evaluation of heterozygosity outliers. Samples failing QC are excluded, as are variants with low call rates or high missingness. Following QC, imputation is performed using the Haplotype Reference Consortium (HRC) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) and TOPMED (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) panels, providing dense genome-wide coverage and enabling accurate calculation of PGS.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eConstruction of PGS\u003csub\u003eBMD\u003c/sub\u003e\u003c/h2\u003e\u003cp\u003ePGS\u003csub\u003eBMD\u003c/sub\u003e will be derived from the largest available genome-wide association study (GWAS) summary statistics identifying loci associated with BMD in populations of European background. The specific BMD phenotype (e.g. femoral neck, lumbar spine, heel, or total body) used for the PGS will be determined based on the most comprehensive and up-to-date GWAS available at the time of score construction, to maximize power and relevance. The PGS will be constructed as a weighted sum of effect alleles across associated loci, with weights corresponding to published effect sizes using PRSice2 (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Scores will be standardized within the genotyped cohort to enable comparison across individuals and strata. Portability across ancestral backgrounds will be monitored to ensure sampling is not biased at the extremes of the PGS distribution with under- or over-representation of specific groups of individuals (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eStratification and recall\u003c/h2\u003e\u003cp\u003eIndividuals across eight clinics are stratified into quartiles of the PGS\u003csub\u003eBMD\u003c/sub\u003e distribution. Those in the highest and lowest quartiles (25% tails) are eligible for recall to Stage 2 seeking to recruit those at the top and lowest 15% tails. Moreover, all patients of the mastocytosis clinic will be invited to Stage 2 without including them in the recall pool. The \u003cem\u003eRbG\u003c/em\u003e strategy ensures efficiency and power by enriching the study sample with individuals at genetic extremes, while maintaining clinical diversity.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003ePatient perceptions and perspectives\u003c/h2\u003e\u003cp\u003eA pilot study of 30 participants, (every 50th patient systematically selected from each clinic), will record via perception questionnaires and semi-structured interviews, their knowledge, attitudes, and beliefs regarding skeletal health, genetics, and research participation. Items address privacy concerns, willingness to engage in genetic research, and understanding of skeletal fragility. Such insights will guide the design of future recruitment strategies and warrant patient-centered care.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eOutcomes, feasibility, and rigor\u003c/h2\u003e\u003cp\u003eThe SINTER study is designed with a dual methodological aim: to evaluate the feasibility of implementing an \u003cem\u003eRbG\u003c/em\u003e framework in a heterogeneous outpatient setting and to test whether multidimensional phenotyping in a \u003cem\u003eRbG\u003c/em\u003e framework can reveal skeletal differences across genetic and clinical strata. Outcomes are therefore structured into primary methodological outcomes and secondary analytic outcomes.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003ePrimary methodological outcomes\u003c/h2\u003e\u003cp\u003eThe first set of outcomes concerns the feasibility and reproducibility of the study design itself:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eRecruitment and recall rates: the proportion of eligible patients consenting to Stage 1 genotyping and subsequently agreeing to Stage 2 recall.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eProtocol adherence: the extent to which participants complete the full multidimensional phenotyping examinations.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eData completeness: proportion of datasets with missing or unusable information across domains.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThese methodological outcomes will determine whether \u003cem\u003eRbG\u003c/em\u003e with multidimensional phenotyping can be executed efficiently and reliably in a busy clinical environment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eSecondary analytic outcomes\u003c/h2\u003e\u003cp\u003eThe second set of outcomes evaluates the scientific yield of the design:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePGS-stratified skeletal phenotypes: differences in aBMD, volumetric density, cortical/trabecular structure, BMSi, mechanography, and alignment indices between the extremes of the PGS\u003csub\u003eBMD\u003c/sub\u003e distribution.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eExploratory associations: integration of lifestyle exposures with skeletal outcomes, and their potential modification by genetic risk.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003eStatistical plan and power\u003c/h2\u003e\u003cdiv id=\"Sec28\" class=\"Section4\"\u003e\u003ch2\u003eAnalytical framework\u003c/h2\u003e\u003cp\u003eThe primary analytical goal is to test whether participants at the extremes of the PGS\u003csub\u003eBMD\u003c/sub\u003e distribution constitute well-defined contrast groups that differ systematically in skeletal parameters captured by the comprehensive phenotypic assessment. Analyses will compare high-PGS and low-PGS groups using linear regression for continuous outcomes (e.g., aBMD, cortical thickness, BMSi).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003eSample size justification\u003c/h2\u003e\u003cp\u003eThe required size of the genotyped pool depends on the correlation (R\u0026sup2;) between PGS and measured BMD, and between BMD and fracture risk. Based on published evidence, we assume:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eR\u0026sup2; between measured BMD and fracture risk\u0026thinsp;\u0026asymp;\u0026thinsp;0.10.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eR\u0026sup2; between PGS\u003csub\u003eBMD\u003c/sub\u003e and measured BMD\u0026thinsp;\u0026asymp;\u0026thinsp;0.06.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eUnder these assumptions, a two-tailed α\u0026thinsp;=\u0026thinsp;0.05 and expected recall rate of 57% provide 80% power to detect differences of 0.15 standard deviations (SD) in BMD-related phenotypes between PGS extremes. With 5,250 successfully genotyped participants, 2,625 individuals are expected to fall into the top and bottom quartiles, of whom 1,500 participate in Stage 2 recall, yielding 750 participants per PGS-defined group.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDetectable effects\u003c/h3\u003e\n\u003cp\u003eWith the above design, the RbG study is powered to detect effect sizes of differences\u0026thinsp;\u0026ge;\u0026thinsp;0.15 SD in skeletal phenotypes (e.g., cortical thickness, trabecular density) for continuous outcomes; and odds ratios of 1.8 for fracture prevalence when comparing PGS extremes, under the assumption of a 5% exposure probability in high-BMD controls for binary outcomes.\u003c/p\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003eProof of concept of the RbG approach\u003c/h2\u003e\u003cp\u003eThe Rotterdam Study, established in 1990 in the Ommoord district of Rotterdam, The Netherlands, is a prospective, population-based cohort study designed to examine the incidence, determinants, and prognosis of chronic diseases in middle-aged and older adults which to date has evaluated close to 18,000 participants(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). In a selection of 5331 participants from RSI, RSII, and RSIII with genotype and phenotype information, a \u003cem\u003eweighted\u003c/em\u003e PGS was constructed for \u003cem\u003etotal body\u003c/em\u003e BMD using 81 SNPs (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Disease biomarkers specific to the conditions under study, together with sex and age were compared between the 25% lower (n\u0026thinsp;=\u0026thinsp;666) and higher tail (n\u0026thinsp;=\u0026thinsp;666) of the BMD\u003csub\u003ePGS\u003c/sub\u003e distribution. Participants in the lower tail had lower femoral-neck BMD and higher fracture prevalence than those at the upper tail, while age, sex, and all other comorbidity factors remained balanced across groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These findings, which are consistent with the Mendelian randomization framework under which PGSs are derived, illustrate that polygenic extremes can preserve unconfounded (randomized) and well-defined contrasts in skeletal outcomes, even in the presence of heterogeneous sampling. This supports the utility and optimal design of \u003cem\u003eRbG\u003c/em\u003e approaches for deeper phenotyping in clinical studies (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e\u003ch2\u003eExpected utility\u003c/h2\u003e\u003cp\u003eSINTER seeks to demonstrate the feasibility of integrating \u003cem\u003eRbG\u003c/em\u003e with multidimensional phenotyping in musculoskeletal research. Such methodology opens paths for a \u0026ldquo;genetics-first\u0026rdquo; approach in musculoskeletal research, where genomic information is used to complement established clinical assessments and to refine hypotheses for future translational or interventional studies. By recalling individuals from the PGS\u003csub\u003eBMD\u003c/sub\u003e extremes, an unconfounded and power-optimized study setting is created, which allows testing whether genetic predisposition translates into measurable differences in skeletal structure, bone material properties, and neuromuscular function that are not readily identified by comparing groups differing in measured BMD alone. Further, recruitment of patients with underlying chronic diseases will provide valuable insights into the interaction between chronic conditions and genetic predisposition, potentially identifying disease-specific skeletal signatures and clarifying why conventional diagnostic and treatment tools often underperform in these groups.\u003c/p\u003e\u003cp\u003eFrom another perspective, the resulting genotyped pool will provide a long-term resource for replication, Mendelian randomization, and expansion to other phenotypes; findings may inform future strategies for genetics-informed risk stratification and targeted phenotyping. Ultimately, findings from SINTER could help define the pathophysiology of skeletal fragility and support the end goal of patient stratification tailoring treatments based on genetic susceptibility and disease risk.\u003c/p\u003e\u003cdiv id=\"Sec34\" class=\"Section3\"\u003e\u003ch2\u003eStrengths and limitations\u003c/h2\u003e\u003cp\u003eThe SINTER study has several methodological strengths. It is the first \u003cem\u003eRbG\u003c/em\u003e design implemented in musculoskeletal outpatient clinics, integrating genetic stratification directly into clinical workflows of patients rather than confining it to a research-based framework. The study employs an extensive multidimensional phenotyping approach, encompassing imaging, tissue-level analysis, functional, systemic, and patient-reported outcomes and thus enabling a comprehensive characterization of skeletal fragility that goes beyond the traditional BMD paradigm. Reproducibility is ensured through standardized procedures and pre-specified analyses. However, there are limitations. The study is conducted at a single tertiary center which may limit generalizability. Furthermore, sample size within comorbidity subgroups may be modest, and the PGS\u003csub\u003eBMD\u003c/sub\u003e is derived from European-ancestry GWAS, potentially reducing utility in underrepresented ancestries. Replication in larger, longitudinal, and more diverse settings will therefore be needed. Genetic and phenotypic data are linked through secure, de-identified databases under GDPR-compliant governance.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of interest:\u003c/h2\u003e\u003cp\u003eF.K, K.H, S.L, K.T, K.B., M.B., L.C, P.v.D., M.v.H., M.H., J.R.v.L., M.M., J.O., B.O., R.P., F.M-R., J.v.R., A.U., W.V., F.R. have no conflict of interests. E.v.V reports honoraria from USB Pharma, Kyowa Kirin and Theromex and Amgen not related to this work. M.C.Z reports honoraria for lectures or consulting from USB Pharma, Kyowa Kirin and Theromex and Amgen not related to this work. E.F.C.v.R reports honoraria from Dutch Obesity Academy, E-wise and Medscape/WebMD not related to this work. L.C has received funding for investigator-initiated research from Abbott Diabetes Care not related to this work.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eSetup and conduction of the SINTER Study together with KH, FK, SL, KT and FR are funded by the European Research Council (ERC) under Horizon 2020 research and innovation programme (Grant agreement No. 101021500 \u0026ndash; LEGENDARE Advance Grant).\u003c/p\u003e\u003ch2\u003eAuthor Contributions:\u003c/h2\u003e\u003cp\u003eFjorda Koromani (Conceptualization, Data curation, Investigation, Methodology, Visualization, Writing\u0026mdash;original draft, Supervision, Writing\u0026mdash;review \u0026amp; editing), Kirsty Huininga (Conceptualization, Data curation, Investigation, Methodology, Visualization, Writing\u0026mdash;original draft, Writing\u0026mdash;review \u0026amp; editing), Siwen Li (Writing \u0026ndash; review \u0026amp; editing), Katerina Trajanoska (Writing \u0026ndash; review \u0026amp; editing), Kirsten Berk (Writing \u0026ndash; review \u0026amp; editing),\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJohnell O, Kanis JA. 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Eur J Epidemiol. 2024;39(2):183\u0026ndash;206.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMedina-Gomez C, Kemp JP, Trajanoska K, Luan J, Chesi A, Ahluwalia TS, et al. Life-Course Genome-wide Association Study Meta-analysis of Total Body BMD and Assessment of Age-Specific Effects. Am J Hum Genet. 2018;102(1):88\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e \u003cstrong\u003eCharacteristics of the study population compared between low and high BMD wPGS.\u003c/strong\u003e As expected, the measured FN-BMD is lower in the low PGS\u003csub\u003eBMD\u0026nbsp;\u003c/sub\u003ecompared to the high PGS\u003csub\u003eBMD\u0026nbsp;\u003c/sub\u003egroup whereas fracture prevalence is higher in the\u003csub\u003e\u0026nbsp;\u003c/sub\u003elow PGS\u003csub\u003eBMD\u0026nbsp;\u003c/sub\u003ecompared to the high PGS\u003csub\u003eBMD\u0026nbsp;\u003c/sub\u003egroup. All other covariates do not differ between groups, thus showing the randomization properties of stratifying PGS.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow BMD wPGS n=666\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh BMD wPGS n=666\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ewPGS BMD (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e4.67 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003e5.72 (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFN-BMD (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e0.83 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003e0.92 (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOPFx (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e204 (30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003e160 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e66.6 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003e66.9 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex (F) (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e466 (70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003e449 (67.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFRAX MOF \u0026gt;10% (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e173 (26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003e164 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2D (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e87 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003e99 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSH \u0026gt;3.85 mU/L (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e90 (13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003e104 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eeGFR \u0026lt;56.5 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e90 (13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003e98 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFalling last month (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e101 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003e108 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI \u0026gt;31 kg/m2 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e92 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003e99 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCholesterol \u0026gt;7.5 mmol/L (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e86 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003e98 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 601px;\"\u003e\n \u003cp\u003ewPGS- weighted polygenic score, BMD- bone mineral density OPFx- prevalent osteoporotic fractures, MOF- major osteoporotic fractures, TSH- thyroid stimulating hormone, eGFR- estimated glomerular filtration rate, BMI- body mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejep","sideBox":"Learn more about [European Journal of Epidemiology](https://www.springer.com/journal/10654)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejep/default.aspx","title":"European Journal of Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"recall by genotype, polygenic score, skeletal fragility, comorbidity, multidimensional phenotyping","lastPublishedDoi":"10.21203/rs.3.rs-8238983/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8238983/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBone mineral density (BMD) measured by DXA remains the clinical standard for diagnosing osteoporosis, but fails to capture the heterogeneity of skeletal fragility, particularly in patients with chronic diseases. We hereby describe the design of the EraSmus medIcal CeNTer skEletal fRagility (SINTER) Study, which integrates polygenic scores for BMD (PGS\u003csub\u003eBMD\u003c/sub\u003e) with a multidimensional phenotyping approach in diverse outpatient clinics, as a methodological framework for musculoskeletal research. The study aims at assessing the additional value of combining genetic information with comprehensive musculoskeletal phenotyping to better elucidate the mechanisms of skeletal fragility in chronic conditions. SINTER is an observational, cross-sectional \u003cem\u003eRecall-by-Genotype\u003c/em\u003e (\u003cem\u003eRbG\u003c/em\u003e) study drawn in two stages: (1) genotyping 5,650 patients from nine internal medicine clinics; (2) recalling 1,500 patients from the extremes of the PGS\u003csub\u003eBMD\u003c/sub\u003e distribution for multidimensional musculoskeletal phenotyping together with 400 patients with a rare condition (mastocytosis). Phenotyping includes imaging (DXA, EOSEdge, pQCT), macro- and tissue-level skeletal properties (ultrasound, reference point indentation), functional assessments (mechanography, handgrip), lifestyle and systemic exposures (diet, physical activity), and patient perceptions obtained via in-depth interview and questionnaires. The study design is unique in combining a \u003cem\u003eRbG\u003c/em\u003e framework with multidimensional phenotyping in outpatient-clinics, enabling systematic evaluation of genetic and disease-specific contributors to skeletal fragility. SINTER establishes a methodological template for \u003cem\u003eRbG\u003c/em\u003e designs in clinical musculoskeletal research while helping elucidate the mechanisms of skeletal fragility.\u003c/p\u003e","manuscriptTitle":"The SINTER study: A Recall-by-Genotype design with multidimensional musculoskeletal phenotyping across internal-medicine outpatient clinics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 10:25:26","doi":"10.21203/rs.3.rs-8238983/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-01-30T09:56:21+00:00","index":0,"fulltext":""},{"type":"editorInvited","content":"European Journal of Epidemiology","date":"2025-12-08T19:48:00+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-03T13:44:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-03T07:05:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Epidemiology","date":"2025-11-29T14:50:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejep","sideBox":"Learn more about [European Journal of Epidemiology](https://www.springer.com/journal/10654)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejep/default.aspx","title":"European Journal of Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d12d1f29-2da5-4a50-bd51-9373779fcad0","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-21T08:02:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 10:25:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8238983","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8238983","identity":"rs-8238983","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}