A graph model to describe the network connectivity of trabecular plates and rods.

Introduction: The trabecular network is perceived as a collection of interconnected plate- (P) and rod-like (R) elements. Previous research has highlighted how these elements and their connectivity influence the mechanical properties of bone, yet further work is required to elucidate better the deeply interconnected nature of the trabecular network with distinct element formations conducting forces per their mechanical boundary conditions. Within this network, forces act through elements: a rod or plate with force applied to one end will transmit this force to a component connected to the other end, defining the boundary conditions for the loading of each element. To that end, this study has two aims: First, to investigate the connectivity of individually segmented elements of trabecular bone with respect to their local boundary conditions as defined by the surrounding trabecular network and linking them directly to the bone's overall mechanical response during loading using a mathematical graph model of the plate and rod (PR) Network. Second, we use this model to quantify side artifacts, a known artifact when testing an excised specimen of trabecular bone, where vertical trabeculae lose their load-bearing capacity due to a loss of connectivity, ultimately resulting in a change of the trabecular network topology. Resuts: Connected elements derived from our model predicted apparent elastic modulus by fitting a linear regression (R 2 = 0.81). In comparison, prediction using conventional bone volume fraction results in a lower accuracy (R 2 = 0.72), demonstrating the ability of the PR Network to estimate compressive elastic modulus independent of specimen size or loading boundary condition. Discussion: PR Network models are a novel approach to describing connectivity within the trabecular network and incorporating mechanical boundary conditions within the morphological analysis, thus enabling the study of intrinsic material properties of trabecular bone. Ultimately, PR Network models may be an early predictor or provide further insights into osteo-degenerative diseases.

Quantitative 1H Magnetic Resonance Imaging on Normal and Pathologic Rat Bones by Solid-State 1H ZTE Sequence with Water and Fat Suppression.

BACKGROUND: Osteoporosis (OP) and osteomalacia (OM) are metabolic bone diseases characterized by mineral and matrix density changes. Quantitative bone matrix density differentiates OM from OP. MRI is a noninvasive and nonionizing imaging technique that can measure bone matrix density quantitatively in ex vivo and in vivo. PURPOSE: To demonstrate water + fat suppressed 1H MRI to compute bone matrix density in ex vivo rat femurs in the preclinical model. STUDY TYPE: Prospective. ANIMAL MODEL: Fifteen skeletally mature female Sprague-Dawley rats, five per group (normal, ovariectomized (OVX), partially nephrectomized/vitamin D (Vit-D) deficient), 250-275 g, ∼15 weeks old. FIELD STRENGTH/SEQUENCE: 7T, zero echo time sequence with water + fat (VAPOR) suppression capability, µCT imaging, and gravimetric measurements. ASSESSMENT: Cortical and trabecular bone segments from normal and disease models were scanned in the same coil along with a dual calibration phantom for quantitative assessment of bone matrix density. STATISTICAL TESTS: ANOVA and linear regression were used for data analysis, with P-values <0.05 statistically significant. RESULTS: The MRI-derived three-density PEG pellet densities have a strong linear relationship with physical density measures (r2 = 0.99). The Vit-D group had the lowest bone matrix density for cortical bone (0.47 ± 0.16 g cm-3), whereas the OVX had the lowest bone matrix density for trabecular bone (0.26 ± 0.04 g cm-3). Gravimetry results confirmed these MRI-based observations for Vit-D cortical (0.51 ± 0.07 g cm-3) and OVX trabecular (0.26 ± 0.03 g cm-3) bone groups. DATA CONCLUSION: Rat femur images were obtained using a modified pulse sequence and a custom-designed double-tuned (1H/31P) transmit-receive solenoid-coil on a 7T preclinical MRI scanner. Phantom experiments confirmed a strong linear relation between MRI-derived and physical density measures and quantitative bone matrix densities in rat femurs from normal, OVX, and Vit-D deficient/partially nephrectomized animals were computed. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Bone remodeling and responsiveness to mechanical stimuli in individuals with type 1 diabetes mellitus.

Type 1 diabetes mellitus (T1DM) has been linked to increased osteocyte apoptosis, local accumulation of mineralized lacunar spaces, and microdamage suggesting an impairment of the mechanoregulation network in affected individuals. Diabetic neuropathy might exacerbate this dysfunction through direct effects on bone turnover, and indirect effects on balance, muscle strength, and gait. However, the in vivo effects of impaired bone mechanoregulation on bone remodeling in humans remain underexplored. This longitudinal cohort study assessed consenting participants with T1DM and varying degree of distal symmetric sensorimotor polyneuropathy (T1DM, n = 20, median age 46.5 yr, eight female) and controls (CTRL; n = 9, median age 59.0 yr, four female) at baseline and 4-yr follow-up. Nerve conduction in participants with T1DM was tested using DPNCheck and bone remodeling was quantified with longitudinal high-resolution peripheral quantitative-computed tomography (HR-pQCT, 82 µm) at the standard distal sites. Local trabecular bone formation (Tb.F) and resorption (Tb.R) sites were captured by implementing 3D rigid image registration of HR-pQCT images, and the mechanical environment across the bone microarchitecture at these sites was simulated using micro-finite element analysis. We calculated odds ratios to determine the likelihood of bone formation (ORF) and resorption (ORR) with increasing/decreasing strain in percent as markers for mechanoregulation. At the distal radius, Tb.F was 47% lower and Tb.R was 59% lower in T1DM participants compared with CTRL (P < .05). Tb.F correlated positively with nerve conduction amplitude (R = 0.69, P < .05) in participants with T1DM and negatively with glycated hemoglobin (HbA1c) (R = -0.45, P < .05). Additionally, ORF was 34% lower and ORR was 18% lower in T1DM compared with CTRL (P < .05). Our findings represent in vivo evidence suggesting that bone remodeling in individuals with T1DM is in a state of low responsiveness to mechanical stimuli, resulting in impaired bone formation and resorption rates; these correlate to the degree of neuropathy and level of diabetes control.

In a healthy adult, the body's skeleton self-repairs­or remodels­itself to maintain its strength. At the microscopic level, this process is orchestrated by cells, called osteocytes, which can sense and respond to local mechanical forces. Recent studies have suggested that type 1 diabetes mellitus (T1DM), a metabolic bone disease, may negatively impact this mechanically regulated process and reduce bone strength. To investigate this further, we utilized novel methods to monitor local changes in bone microstructure over time using high­resolution peripheral quantitative­computed tomography, allowing us to study the results of cellular behavior on bone remodeling in participants over time. Our study found that bone formation was 47% lower and bone resorption was 59% lower in participants with T1DM compared with controls (CTRL). Bone formation correlated positively with peripheral nerve function and negatively with glycaemic control in participants with T1DM. Furthermore, the links between mechanical forces acting on bone remodeling were 34% weaker for formation and 18% weaker for resorption compared with CTRL. Our findings show that bone remodeling in people with T1DM is in a state of low responsiveness to mechanical stimuli, resulting in impaired bone formation and resorption rates, and ultimately, impaired self-repair.

Quantitative 31P magnetic resonance imaging on pathologic rat bones by ZTE at 7T.

BACKGROUND: Osteoporosis is characterized by low bone mineral density (BMD), which predisposes individuals to frequent fragility fractures. Quantitative BMD measurements can potentially help distinguish bone pathologies and allow clinicians to provide disease-relieving therapies. Our group has developed non-invasive and non-ionizing magnetic resonance imaging (MRI) techniques to measure bone mineral density quantitatively. Dual-energy X-ray Absorptiometry (DXA) is a clinically approved non-invasive modality to diagnose osteoporosis but has associated disadvantages and limitations. PURPOSE: Evaluate the clinical feasibility of phosphorus (31P) MRI as a non-invasive and non-ionizing medical diagnostic tool to compute bone mineral density to help differentiate between different metabolic bone diseases. MATERIALS AND METHODS: Fifteen ex-vivo rat bones in three groups [control, ovariectomized (osteoporosis), and vitamin-D deficient (osteomalacia - hypo-mineralized) were scanned to compute BMD. A double-tuned (1H/31P) transmit-receive single RF coil was custom-designed and in-house-built with a better filling factor and strong radiofrequency (B1) field to acquire solid-state 31P MR images from rat femurs with an optimum signal-to-noise ratio (SNR). Micro-computed tomography (µCT) and gold-standard gravimetric analyses were performed to compare and validate MRI-derived bone mineral densities. RESULTS: Three-dimensional 31P MR images of rat bones were obtained with a zero-echo-time (ZTE) sequence with 468 µm spatial resolution and 12-17 SNR on a Bruker 7 T Biospec having multinuclear capability. BMD was measured quantitatively on cortical and trabecular bones with a known standard reference. A strong positive correlation (R = 0.99) and a slope close to 1 in phantom measurements indicate that the densities measured by 31P ZTE MRI are close to the physical densities in computing quantitative BMD. The 31P NMR properties (resonance linewidth of 4 kHz and T1 of 67 s) of ex-vivo rat bones were measured, and 31P ZTE imaging parameters were optimized. The BMD results obtained from MRI are in good agreement with µCT and gravimetry results. CONCLUSION: Quantitative measurements of BMD on ex-vivo rat femurs were successfully conducted on a 7 T preclinical scanner. This study suggests that quantitative measurements of BMD are feasible on humans in clinical MRI with suitable hardware, RF coils, and pulse sequences with optimized parameters within an acceptable scan time since human femurs are approximately ten times larger than rat femurs. As MRI provides quantitative in-vivo data, various systemic musculoskeletal conditions can be diagnosed potentially in humans.

A multi-stack registration technique to improve measurement accuracy and precision across longitudinal HR-pQCT scans.

BACKGROUND: Recent applications of high-resolution peripheral quantitative computed tomography (HR-pQCT) have demonstrated that changes in local bone remodelling can be quantified in vivo using longitudinal three-dimensional image registration. However, certain emerging applications, such as fracture healing and joint analysis, require larger multi-stack scan regions that can result in stack shift image artifacts. These artifacts can be detrimental to the accurate alignment of the bone structure across multiple timepoints. The purpose of this study was to establish a multi-stack registration protocol for evaluating longitudinal HR-pQCT images and to assess the accuracy and precision error in comparison with measures obtained using previously established three-dimensional longitudinal registration. METHODS: Three same day multi-stack HR-pQCT scans of the radius (2 stacks in length) and tibia (3 stacks in length) were obtained from 39 healthy individuals who participated in a previous reproducibility study. A fully automated multi-stack registration algorithm was developed to re-align stacks within a scan by leveraging slight offsets between longitudinal scans. Stack shift severity before and after registration was quantified using a newly proposed stack-shift severity score. The false discovery rate for bone remodelling events and precision error of bone morphology and micro-finite element analysis parameters were compared between longitudinally registered scans with and without the addition of multi-stack registration. RESULTS: Most scans (82 %) improved in stack alignment or maintained the lowest stack shift severity score when multi-stack registration was implemented. The false discovery rate of bone remodelling events significantly decreased after multi-stack registration, resulting in median false detection of bone formation and resorption fractions between 3.2 to 7.5 % at the radius and 3.4 to 5.3 % at the tibia. Further, precision error was significantly reduced or remained unchanged in all standard bone morphology and micro-finite element analysis parameters, except for total and trabecular cross-sectional areas. CONCLUSION: Multi-stack registration is an effective strategy for accurately aligning multi-stack HR-pQCT scans without modification of the image acquisition protocol. The algorithm presented here is a viable approach for performing accurate morphological analysis on multi-stack HR-pQCT scans, particularly for advanced application investigating local bone remodelling in vivo.

Precision of bone mechanoregulation assessment in humans using longitudinal high-resolution peripheral quantitative computed tomography in vivo.

Local mechanical stimuli in the bone microenvironment are essential for the homeostasis and adaptation of the skeleton, with evidence suggesting that disruption of the mechanically-driven bone remodelling process may lead to bone loss. Longitudinal clinical studies have shown the combined use of high-resolution peripheral quantitative computed tomography (HR-pQCT) and micro-finite element analysis can be used to measure load-driven bone remodelling in vivo; however, quantitative markers of bone mechanoregulation and the precision of these analyses methods have not been validated in human subjects. Therefore, this study utilised participants from two cohorts. A same-day cohort (n = 33) was used to develop a filtering strategy to minimise false detections of bone remodelling sites caused by noise and motion artefacts present in HR-pQCT scans. A longitudinal cohort (n = 19) was used to develop bone imaging markers of trabecular bone mechanoregulation and characterise the precision for detecting longitudinal changes in subjects. Specifically, we described local load-driven formation and resorption sites independently using patient-specific odds ratios (OR) and 99 % confidence intervals. Conditional probability curves were computed to link the mechanical environment to the remodelling events detected on the bone surface. To quantify overall mechanoregulation, we calculated a correct classification rate measuring the fraction of remodelling events correctly identified by the mechanical signal. Precision was calculated as root-mean-squared averages of the coefficient of variation (RMS-SD) of repeated measurements using scan-rescan pairs at baseline combined with a one-year follow-up scan. We found no significant mean difference (p < 0.01) between scan-rescan conditional probabilities. RMS-SD was 10.5 % for resorption odds, 6.3 % for formation odds, and 1.3 % for correct classification rates. Bone was most likely to be formed in high-strain and resorbed in low-strain regions for all participants, indicating a consistent, regulated response to mechanical stimuli. For each percent increase in strain, the likelihood of bone resorption decreased by 2.0 ± 0.2 %, and the likelihood of bone formation increased by 1.9 ± 0.2 %, totalling 38.3 ± 1.1 % of strain-driven remodelling events across the entire trabecular compartment. This work provides novel robust bone mechanoregulation markers and their precision for designing future clinical studies.

Mechanostat parameters estimated from time-lapsed in vivo micro-computed tomography data of mechanically driven bone adaptation are logarithmically dependent on loading frequency.

Mechanical loading is a key factor governing bone adaptation. Both preclinical and clinical studies have demonstrated its effects on bone tissue, which were also notably predicted in the mechanostat theory. Indeed, existing methods to quantify bone mechanoregulation have successfully associated the frequency of (re)modeling events with local mechanical signals, combining time-lapsed in vivo micro-computed tomography (micro-CT) imaging and micro-finite element (micro-FE) analysis. However, a correlation between the local surface velocity of (re)modeling events and mechanical signals has not been shown. As many degenerative bone diseases have also been linked to impaired bone (re)modeling, this relationship could provide an advantage in detecting the effects of such conditions and advance our understanding of the underlying mechanisms. Therefore, in this study, we introduce a novel method to estimate (re)modeling velocity curves from time-lapsed in vivo mouse caudal vertebrae data under static and cyclic mechanical loading. These curves can be fitted with piecewise linear functions as proposed in the mechanostat theory. Accordingly, new (re)modeling parameters can be derived from such data, including formation saturation levels, resorption velocity moduli, and (re)modeling thresholds. Our results revealed that the norm of the gradient of strain energy density yielded the highest accuracy in quantifying mechanoregulation data using micro-finite element analysis with homogeneous material properties, while effective strain was the best predictor for micro-finite element analysis with heterogeneous material properties. Furthermore, (re)modeling velocity curves could be accurately described with piecewise linear and hyperbola functions (root mean square error below 0.2 µm/day for weekly analysis), and several (re)modeling parameters determined from these curves followed a logarithmic relationship with loading frequency. Crucially, (re)modeling velocity curves and derived parameters could detect differences in mechanically driven bone adaptation, which complemented previous results showing a logarithmic relationship between loading frequency and net change in bone volume fraction over 4 weeks. Together, we expect this data to support the calibration of in silico models of bone adaptation and the characterization of the effects of mechanical loading and pharmaceutical treatment interventions in vivo.

Motion grading of high-resolution quantitative computed tomography supported by deep convolutional neural networks.

Image quality degradation due to subject motion confounds the precision and reproducibility of measurements of bone density, morphology and mechanical properties from high-resolution peripheral quantitative computed tomography (HR-pQCT). Time-consuming operator-based scoring of motion artefacts remains the gold standard to determine the degree of acceptable motion. However, due to the subjectiveness of manual grading, HR-pQCT scans of poor quality, which cannot be used for analysis, may be accepted upon initial review, leaving patients with incomplete or inaccurate imaging results. Convolutional Neural Networks (CNNs) enable fast image analysis with relatively few pre-processing requirements in an operator-independent and fully automated way for image classification tasks. This study aimed to develop a CNN that can predict motion scores from HR-pQCT images, while also being aware of uncertain predictions that require manual confirmation. The CNN calculated motion scores within seconds and achieved a high F1-score (86.8 ± 2.8 %), with good precision (87.5 ± 2.7 %), recall (86.7 ± 2.9 %) and a substantial agreement with the ground truth measured by Cohen's kappa (κ = 68.6 ± 6.2 %); motion scores of the test dataset were predicted by the algorithm with comparable accuracy, precision, sensitivity and agreement as by the operators (p > 0.05). This post-processing approach may be used to assess the effect of motion scores on microstructural analysis and can be immediately implemented into clinical protocols, significantly reducing the time for quality assessment and control of HR-pQCT scans.

Meta-analysis of Diabetes Mellitus-Associated Differences in Bone Structure Assessed by High-Resolution Peripheral Quantitative Computed Tomography.

PURPOSE OF REVIEW: Diabetes mellitus is defined by elevated blood glucose levels caused by changes in glucose metabolism and, according to its pathogenesis, is classified into type 1 (T1DM) and type 2 (T2DM) diabetes mellitus. Diabetes mellitus is associated with multiple degenerative processes, including structural alterations of the bone and increased fracture risk. High-resolution peripheral computed tomography (HR-pQCT) is a clinically applicable, volumetric imaging technique that unveils bone microarchitecture in vivo. Numerous studies have used HR-pQCT to assess volumetric bone mineral density and microarchitecture in patients with diabetes, including characteristics of trabecular (e.g. number, thickness and separation) and cortical bone (e.g. thickness and porosity). However, study results are heterogeneous given different imaging regions and diverse patient cohorts. RECENT FINDINGS: This meta-analysis assessed T1DM- and T2DM-associated characteristics of bone microarchitecture measured in human populations in vivo reported in PubMed- and Embase-listed publications from inception (2005) to November 2021. The final dataset contained twelve studies with 516 participants with T2DM and 3067 controls and four studies with 227 participants with T1DM and 405 controls. While T1DM was associated with adverse trabecular characteristics, T2DM was primarily associated with adverse cortical characteristics. These adverse effects were more severe at the radius than the load-bearing tibia, indicating increased mechanical loading may compensate for deleterious bone microarchitecture changes and supporting mechanoregulation of bone fragility in diabetes mellitus. Our meta-analysis revealed distinct predilection sites of bone structure aberrations in T1DM and T2DM, which provide a foundation for the development of animal models of skeletal fragility in diabetes and may explain the uncertainty of predicting bone fragility in diabetic patients using current clinical algorithms.

Bone Mechanoregulation Allows Subject-Specific Load Estimation Based on Time-Lapsed Micro-CT and HR-pQCT in Vivo.

Patients at high risk of fracture due to metabolic diseases frequently undergo long-term antiresorptive therapy. However, in some patients, treatment is unsuccessful in preventing fractures or causes severe adverse health outcomes. Understanding load-driven bone remodelling, i.e., mechanoregulation, is critical to understand which patients are at risk for progressive bone degeneration and may enable better patient selection or adaptive therapeutic intervention strategies. Bone microarchitecture assessment using high-resolution peripheral quantitative computed tomography (HR-pQCT) combined with computed mechanical loads has successfully been used to investigate bone mechanoregulation at the trabecular level. To obtain the required mechanical loads that induce local variances in mechanical strain and cause bone remodelling, estimation of physiological loading is essential. Current models homogenise strain patterns throughout the bone to estimate load distribution in vivo, assuming that the bone structure is in biomechanical homoeostasis. Yet, this assumption may be flawed for investigating alterations in bone mechanoregulation. By further utilising available spatiotemporal information of time-lapsed bone imaging studies, we developed a mechanoregulation-based load estimation (MR) algorithm. MR calculates organ-scale loads by scaling and superimposing a set of predefined independent unit loads to optimise measured bone formation in high-, quiescence in medium-, and resorption in low-strain regions. We benchmarked our algorithm against a previously published load history (LH) algorithm using synthetic data, micro-CT images of murine vertebrae under defined experimental in vivo loadings, and HR-pQCT images from seven patients. Our algorithm consistently outperformed LH in all three datasets. In silico-generated time evolutions of distal radius geometries (n = 5) indicated significantly higher sensitivity, specificity, and accuracy for MR than LH (p < 0.01). This increased performance led to substantially better discrimination between physiological and extra-physiological loading in mice (n = 8). Moreover, a significantly (p < 0.01) higher association between remodelling events and computed local mechanical signals was found using MR [correct classification rate (CCR) = 0.42] than LH (CCR = 0.38) to estimate human distal radius loading. Future applications of MR may enable clinicians to link subtle changes in bone strength to changes in day-to-day loading, identifying weak spots in the bone microstructure for local intervention and personalised treatment approaches.

Application of subject-specific adaptive mechanical loading for bone healing in a mouse tail vertebral defect.

Methods to repair bone defects arising from trauma, resection, or disease, continue to be sought after. Cyclic mechanical loading is well established to influence bone (re)modelling activity, in which bone formation and resorption are correlated to micro-scale strain. Based on this, the application of mechanical stimulation across a bone defect could improve healing. However, if ignoring the mechanical integrity of defected bone, loading regimes have a high potential to either cause damage or be ineffective. This study explores real-time finite element (rtFE) methods that use three-dimensional structural analyses from micro-computed tomography images to estimate effective peak cyclic loads in a subject-specific and time-dependent manner. It demonstrates the concept in a cyclically loaded mouse caudal vertebral bone defect model. Using rtFE analysis combined with adaptive mechanical loading, mouse bone healing was significantly improved over non-loaded controls, with no incidence of vertebral fractures. Such rtFE-driven adaptive loading regimes demonstrated here could be relevant to clinical bone defect healing scenarios, where mechanical loading can become patient-specific and more efficacious. This is achieved by accounting for initial bone defect conditions and spatio-temporal healing, both being factors that are always unique to the patient.

Two Colour Photoflow Chemistry for Macromolecular Design.

We report a photochemical flow setup that exploits λ-orthogonal reactions using two different colours of light (λ1 =350 nm and λ2 =410 nm) in sequential on-line irradiation steps. Critically, both photochemically reactive units (a visible-light reactive chalcone and a UV-activated photo-caged diene) are present in the reaction mixture. We demonstrate the power of two colour photoflow by the wavelength-selective end group modification of photo-caged polymer end groups and the subsequent polymer ring closure driven by a [2+2] cycloaddition. Importantly, we evidence that the high energy gate does not induce the visible light reaction of the chalcone, which attests the true λ-orthogonal nature of the flow reaction system. For the first time, this study opens the realm of photoflow reactions to λ-orthogonal photochemistry.

Targeted and modular architectural polymers employing bioorthogonal chemistry for quantitative therapeutic delivery.

There remain several key challenges to existing therapeutic systems for cancer therapy, such as quantitatively determining the true, tissue-specific drug release profile in vivo, as well as reducing side-effects for an increased standard of care. Hence, it is crucial to engineer new materials that allow for a better understanding of the in vivo pharmacokinetic/pharmacodynamic behaviours of therapeutics. We have expanded on recent "click-to-release" bioorthogonal pro-drug activation of antibody-drug conjugates (ADCs) to develop a modular and controlled theranostic system for quantitatively assessing site-specific drug activation and deposition from a nanocarrier molecule, by employing defined chemistries. The exploitation of quantitative imaging using positron emission tomography (PET) together with pre-targeted bioorthogonal chemistries in our system provided an effective means to assess in real-time the exact amount of active drug administered at precise sites in the animal; our methodology introduces flexibility in both the targeting and therapeutic components that is specific to nanomedicines and offers unique advantages over other technologies. In this approach, the in vivo click reaction facilitates pro-drug activation as well as provides a quantitative means to investigate the dynamic behaviour of the therapeutic agent.

To what extent are psychiatrists aware of the comorbid somatic illnesses of their patients with serious mental illnesses? - a cross-sectional secondary data analysis.

BACKGROUND: Somatic comorbidities are a serious problem in patients with severe mental illnesses. These comorbidities often remain undiagnosed for a long time. In Germany, physicians are not allowed to access patients' health insurance data and do not have routine access to documentation from other providers of health care. Against this background, the objective of this article was to investigate psychiatrists' knowledge of relevant somatic comorbidities in their patients with severe mental illnesses. METHODS: Cross-sectional secondary data analysis was performed using primary data from a prospective study evaluating a model of integrated care of patients with serious mental illnesses. The primary data were linked with claims data from health insurers. Patients' diagnoses were derived on the basis of the ICD-10 and the Anatomical Therapeutic Chemical (ATC) classification system. Diabetes, hypertension, coronary artery disease (CAD), hyperlipidaemia, glaucoma, osteoporosis, polyarthritis and chronic obstructive pulmonary disease (COPD) were selected for evaluation. We compared the number of diagnoses reported in the psychiatrists' clinical report forms with those in the health insurance data. RESULTS: The study evaluated records from 1,195 patients with severe mental illnesses. The frequency of documentation of hypertension ranged from 21% in claims data to 4% in psychiatrists' documentation, for COPD from 12 to 0%, respectively, and for diabetes from 7 to 2%, respectively. The percentage of diagnoses deduced from claims data but not documented by psychiatrists ranged from 68% for diabetes and 83% for hypertension, to 90% for CAD to 98% for COPD. CONCLUSIONS: The majority of psychiatrists participating in the integrated care programme were insufficiently aware of the somatic comorbidities of their patients. We support allowing physicians to access patients' entire medical records to increase their knowledge of patients' medical histories and, consequently, to increase the safety and quality of care.