Plasma metabolites associated with exposure to perfluoroalkyl substances and risk of type 2 diabetes - A nested case-control study

Perfluoroalkyl substances (PFAS) are widespread persistent environmental pollutants. There is evidence that PFAS induce metabolic perturbations in humans, but underlying mechanisms are still unknown. In this exploratory study, we investigated PFAS-related plasma metabolites for their associations with type 2 diabetes (T2D) to gain potential mechanistic insight in these perturbations.We used untargeted LC-MS metabolomics to find metabolites related to PFAS exposures in a case-control study on T2D (n = 187 matched pairs) nested within the Vasterbotten Intervention Programme cohort. Following principal component analysis (PCA), six PFAS measured in plasma appeared in two groups: 1) perfluorononanoic acid, perfluorodecanoic acid and perfluoroundecanoic acid and 2) perfluorohexane sulfonic acid, perfluorooctane sulfonic acid and perfluorooctanoic acid. Using a random forest algorithm, we discovered metabolite features associated with individual PFAS and PFAS exposure groups which were subsequently investigated for associations with risk of T2D.PFAS levels correlated with 171 metabolite features (0.16 <= vertical bar r vertical bar <= 0.37, false discovery rate (FDR) adjusted p < 0.05). Out of these, 35 associated with T2D (p < 0.05), with 7 remaining after multiple testing adjustment (FDR < 0.05). PCA of the 35 PFAS- and T2D-related metabolite features revealed two patterns, dominated by glycerophospholipids and diacylglycerols, with opposite T2D associations. The glycerophospholipids correlated positively with PFAS and associated inversely with risk for T2D (Odds Ratio (OR) per 1 standard deviation (1-SD) increase in metabolite PCA pattern score = 0.2; 95% Confidence Interval (CI) = 0.1-0.4). The diacylglycerols also correlated positively with PFAS, but they associated with increased risk for T2D (OR per 1-SD = 1.9; 95% CI = 1.3-2.7). These results suggest that PFAS associate with two groups of lipid species with opposite relations to T2D risk

Humeral fractures - Epidemiology, treatment and reoperations in the Swedish Fracture Register

Fractures are common, but the knowledge on outcomes, treatment methods or the actual number of fractures treated each year has been sparse. With the introduction of the Swedish Fracture Register (SFR) in 2011, the prospective registration and analysis of detailed population-based fracture data was made possible for the first time. Aim: The overall aim of this thesis was to analyse the quality of data in the SFR and to determine whether, and to which extent, the SFR can be used in epidemiological research. Particularly, the incidence of humeral fractures and the mortality associated with proximal humeral fractures were analysed, and changes in treatment practice for proximal humeral fractures in recent years were evaluated. Methods: All the papers in this thesis were based on fracture registrations in the SFR. Comparisons with other data sources were made; fracture registrations in the National Patient Register (NPR) were used to examine data quality, and information from Statistics Sweden were used to calculate fracture incidence and to compare mortality rates between fracture patients and the general population. Result: In this thesis, Study I demonstrates that 88% of humeral fractures were registered in the SFR, and that all registrations were valid fracture registrations. The SFR therefore constitutes a complete, accurate and efficient source of information, well suited to epidemiological research. In contrast, data from the NPR contain a large proportion of non-valid fracture registrations and need to be improved in order to function as a solid basis for epidemiological research. Studies II-IV in this thesis demonstrate that the incidence of humeral fractures, regardless of fractured segment, increases significantly after the age of 50 years and is predominantly related to low-energy falls. This indicates the important influence of age-related risk factors, such as osteoporosis. Moreover, a proximal humeral fracture is associated with a substantially increased mortality, especially within the first weeks following the fracture. Male gender and low-energy trauma mechanisms were two independent risk factors for death following a humeral fracture. Finally, there was no significant change in the overall proportion of patients treated surgically between 2011 and 2017. However, considerable changes within the different surgical treatment modalities were observed. The use of plate fixation decreased significantly, while the use of intramedullary nails and reversed shoulder arthroplasty increased. Unfortunately, these changes in treatment practice did not affect the reoperation rate, which continued to be high throughout the study period. Conclusion: The SFR is a reliable tool for population-based observational research. Data from the SFR demonstrate that proximal humeral fractures predominately affect frail people. A surprisingly high reoperation rate calls for awareness of the importance of choosing the right treatment to the right patient

Patients with more complex ankle fractures are associated with poorer patient-reported outcome : an observational study of 11,733 patients from the Swedish Fracture Register

Background and purpose: Patient-reported outcome measures (PROMs) following ankle fractures, including all fracture types, have not been reported. It is therefore unclear whether fracture morphology correlates with outcome. We aimed to analyze PROMs in patients with an ankle fracture in relation to the Arbeitsgemeinschaft für Osteosynthesefragen/Orthopaedic Trauma Association (AO/OTA) fracture classification using population-based register data from the Swedish Fracture Register (SFR). Methods: All patients aged ≥ 18 years with an ankle fracture (AO/OTA 44A1–C3) registered in the SFR between 2012 and 2019 were retrieved from the register. Patients with completed PROM questionnaires (Short Musculoskeletal Function Assessment and EuroQol-Visual Analogue Scale) on both day 0 (pre-trauma) and 1-year post-trauma were included. The difference in PROMs between day 0 and 1 year was calculated for each patient (delta value) and mean delta values were calculated at group level, based on the AO/OTA fracture classification. Results: 11,733 patients with 11,751 fractures with complete PROMs were included. According to the AO/OTA classification, 21% were A fractures, 67% were B fractures and 12% were C fractures. All groups of patients, regardless of fracture class (A1–C3), displayed an impairment in PROMs after 1 year compared with day 0. Type C fractures displayed a larger impairment in PROMs at group level than type B, which in turn had a greater impairment than type A. The same pattern was seen in groups 3, 2, and 1 for A and B fractures. Conclusion: We found that the AO/OTA classification is prognostic, where more complex fractures were associated with poorer PROMs

Plasma metabolites associated with healthy Nordic dietary indexes and risk of type 2 diabetes-a nested case-control study in a Swedish population

Background: Epidemiologic evidence on the association of a healthy Nordic diet and future type 2 diabetes (T2D) is limited. Exploring metabolites as biomarkers of healthy Nordic dietary patterns may facilitate investigation of associations between such patterns and T2D. Objectives: We aimed to identify metabolites related to a priori-defined healthy Nordic dietary indexes, the Baltic Sea Diet Score (BSDS) and Healthy Nordic Food Index (HNFI), and evaluate associations with the T2D risk in a case-control study nested in a Swedish population-based prospective cohort. Design: Plasma samples from 421 case-control pairs at baseline and samples from a subset of 151 healthy controls at a 10-y follow-up were analyzed with the use of untargeted liquid chromatography-mass spectrometry metabolomics. Index-related metabolites were identified through the use of random forest modelling followed by partial correlation analysis adjustment for lifestyle confounders. Metabolite patterns were derived via principal component analysis (PCA). ORs of T2D were estimated via conditional logistic regression. Reproducibility of metabolites was assessed by intraclass correlation (ICC) in healthy controls. Associations were also assessed for 10 metabolites previously identified as linking a healthy Nordic diet with T2D. Results: In total, 31 metabolites were associated with BSDS and/or HNFI (-0.19 &lt;= r &lt;= 0.21, 0.10 &lt;= ICC &lt;= 0.59). Two PCs were determined from index-related metabolites: PC1 strongly correlated to the indexes (r = 0.27 for BSDS, r = 0.25 for HNFI, ICC = 0.45) but showed no association with T2D risk. PC2 was weakly associated with the indexes, but more strongly with foods not part of the indexes, e.g., pizza, sausages, and hamburgers. PC2 was also significantly associated with T2D risk. Predefined metabolites were confirmed to be reflective of consumption of whole grains, fish, or vegetables, but not related to T2D risk. Conclusions: Our study did not support an association between healthy Nordic dietary indexes and T2D. However, foods such as hamburger, sausage, and pizza not covered by the indexes appeared to be more important for T2D risk in the current population

Intraindividual Long-term Immune Marker Stability in Plasma Samples Collected in Median 9.4 Years Apart in 304 Adult Cancer-free Individuals

Background: Changes in immune marker levels in the blood could be used to improve the early detection of tumor-associated inflammatory processes. To increase predictiveness and utility in cancer detection, intraindividual long-term stability in cancer-free individuals is critical for biomarker candidates as to facilitate the detection of deviation from the norm. Methods: We assessed intraindividual long-term stability for 19 immune markers (IL10, IL13, TNFa, CXCL13, MCP-3, MIP-1a, MIP-1b, fractalkine, VEGF, FGF-2, TGFa, sIL2Ra, sIL6R, sVEGF-R2, sTNF-R1, sTNF-R2, sCD23, sCD27, and sCD30) in 304 cancer-free individuals. Repeated blood samples were collected up to 20 years apart. Intraindividual reproducibility was assessed by calculating intraclass correlation coefficients (ICC) using a linear mixed model. Results: ICCs indicated fair to good reproducibility (ICCs ≥ 0.40 and < 0.75) for 17 of 19 investigated immune markers, including IL10, IL13, TNFa, CXCL13, MCP-3, MIP-1a, MIP-1b, fractalkine, VEGF, FGF-2, TGFa, sIL2Ra, sIL6R, sTNF-R1, sTNF-R2, sCD27, and sCD30. Reproducibility was strong (ICC ≥ 0.75) for sCD23, while reproducibility was poor (ICC < 0.40) for sVEGF-R2. Using a more stringent criterion for reproducibility (ICC ≥ 0.55), we observed either acceptable or better reproducibility for IL10, IL13, CXCL13, MCP-3, MIP-1a, MIP-1b, VEGF, FGF-2, sTNF-R1, sCD23, sCD27, and sCD30. Conclusions: IL10, IL13, CXCL13, MCP-3, MIP-1a, MIP-1b, VEGF, FGF-2, sTNF-R1, sCD23, sCD27, and sCD30 displayed ICCs consistent with intraindividual long-term stability in cancer-free individuals. Impact: Our data support using these markers in prospective longitudinal studies seeking early cancer detection biomarkers

The Swedish Fracture Register - ten years of experience and 600,000 fractures collected in a National Quality Register

Background Before the creation of the Swedish Fracture Register (SFR), there was no national quality register that prospectively collects data regarding all types of fractures regardless of treatment in an emergency setting. Observational data on fractures registered in a sustainable way may provide invaluable tools for quality improvements in health care and research. Description Ten years after its implementation, the Swedish Fracture Register has 100% coverage among orthopaedic and trauma departments in Sweden. The completeness of registrations reached in 2020 69-96% for hip fractures at the different departments, with the majority reporting a completeness above 85%. The Swedish Fracture Register is a fully web-based national quality register created and run by orthopaedic professionals, with financial support from public healthcare providers and the government. All users have full access to both the registration platform and all aggregated statistics in real time. The web-based platform was created for use in health quality registers and it has easily gained acceptance among users. The register has gradually developed by the addition of more fracture types and skeletal parts. Research activity is high and 31 scientific publications have been published since 2016. The strategy from the start was to publish validation data and basic epidemiological data. However, over the past few years, publications on outcomes, such as re-operations and mortality, have been published and four register-based, randomised, controlled trials are ongoing. Conclusion It is possible to create a fracture register, to gain professional acceptance and to collect fracture data in a sustainable way on a national level if the platform is easy to use. Such a platform can also be used as a randomisation platform for prospective studies

Intraindividual Long-term Immune Marker Stability in Plasma Samples Collected in Median 9.4 Years Apart in 304 Adult Cancer-free Individuals

Background: Changes in immune marker levels in the blood could be used to improve the early detection of tumor-associated inflammatory processes. To increase predictiveness and utility in cancer detection, intraindividual long-term stability in cancer-free individuals is critical for biomarker candidates as to facilitate the detection of deviation from the norm. Methods: We assessed intraindividual long-term stability for 19 immune markers (IL10, IL13, TNFa, CXCL13, MCP-3, MIP-1a, MIP-1b, fractalkine, VEGF, FGF-2, TGFa, sIL2Ra, sIL6R, sVEGF-R2, sTNF-R1, sTNF-R2, sCD23, sCD27, and sCD30) in 304 cancer-free individuals. Repeated blood samples were collected up to 20 years apart. Intraindividual reproducibility was assessed by calculating intraclass correlation coefficients (ICC) using a linear mixed model. Results: ICCs indicated fair to good reproducibility (ICCs ≥ 0.40 and < 0.75) for 17 of 19 investigated immune markers, including IL10, IL13, TNFa, CXCL13, MCP-3, MIP-1a, MIP-1b, fractalkine, VEGF, FGF-2, TGFa, sIL2Ra, sIL6R, sTNF-R1, sTNF-R2, sCD27, and sCD30. Reproducibility was strong (ICC ≥ 0.75) for sCD23, while reproducibility was poor (ICC < 0.40) for sVEGF-R2. Using a more stringent criterion for reproducibility (ICC ≥ 0.55), we observed either acceptable or better reproducibility for IL10, IL13, CXCL13, MCP-3, MIP-1a, MIP-1b, VEGF, FGF-2, sTNF-R1, sCD23, sCD27, and sCD30. Conclusions: IL10, IL13, CXCL13, MCP-3, MIP-1a, MIP-1b, VEGF, FGF-2, sTNF-R1, sCD23, sCD27, and sCD30 displayed ICCs consistent with intraindividual long-term stability in cancer-free individuals. Impact: Our data support using these markers in prospective longitudinal studies seeking early cancer detection biomarkers

Plasma metabolites associated with type 2 diabetes in a Swedish population : a case-control study nested in a prospective cohort

Aims/hypothesis: The aims of the present work were to identify plasma metabolites that predict future type 2 diabetes, to investigate the changes in identified metabolites among individuals who later did or did not develop type 2 diabetes over time, and to assess the extent to which inclusion of predictive metabolites could improve risk prediction. Methods: We established a nested case-control study within the Swedish prospective population-based Vasterbotten Intervention Programme cohort. Using untargeted liquid chromatography-MS metabolomics, we analysed plasma samples from 503 case-control pairs at baseline (a median time of 7 years prior to diagnosis) and samples from a subset of 187 case-control pairs at 10 years of follow-up. Discriminative metabolites between cases and controls at baseline were optimally selected using a multivariate data analysis pipeline adapted for large-scale metabolomics. Conditional logistic regression was used to assess associations between discriminative metabolites and future type 2 diabetes, adjusting for several known risk factors. Reproducibility of identified metabolites was estimated by intra-class correlation over the 10 year period among the subset of healthy participants; their systematic changes over time in relation to diagnosis among those who developed type 2 diabetes were investigated using mixed models. Risk prediction performance of models made from different predictors was evaluated using area under the receiver operating characteristic curve, discrimination improvement index and net reclassification index. Results: We identified 46 predictive plasma metabolites of type 2 diabetes. Among novel findings, phosphatidylcholines (PCs) containing odd-chain fatty acids (C19: 1 and C17:0) and 2-hydroxyethanesulfonate were associated with the likelihood of developing type 2 diabetes; we also confirmed previously identified predictive biomarkers. Identified metabolites strongly correlated with insulin resistance and/or beta cell dysfunction. Of 46 identified metabolites, 26 showed intermediate to high reproducibility among healthy individuals. Moreover, PCs with odd-chain fatty acids, branched-chain amino acids, 3-methyl-2-oxovaleric acid and glutamate changed over time along with disease progression among diabetes cases. Importantly, we found that a combination of five of the most robustly predictive metabolites significantly improved risk prediction if added to models with an a priori defined set of traditional risk factors, but only a marginal improvement was achieved when using models based on optimally selected traditional risk factors. Conclusions/interpretation: Predictive metabolites may improve understanding of the pathophysiology of type 2 diabetes and reflect disease progression, but they provide limited incremental value in risk prediction beyond optimal use of traditional risk factors