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Abstract Aims To study differences in cardiovascular prevention and hypertension management in primary care in men and women, with comparisons between public and privately operated primary health care (PHC). Methods We used register data from Region Stockholm on collected prescribed medication and registered diagnoses, to identify patients aged 30 years and above with hypertension. Age-adjusted logistic regression was used to calculate odds ratios (ORs) with 99% confidence intervals (99% CIs) using public PHC centers as referents. Results In total, 119,267 patients with a registered hypertension diagnosis at their primary care center were included; 58,239 men and 61,028 women. In terms of co-morbidities and medications, there were some differences between privately and publicly run PHC: registered diagnosis of dementia, which was higher at private PHC, age-adjusted OR 1.80 (1.24–2.69). For lifestyle counseling, privately run PHC had a higher rate of registered counseling for tobacco 1.17 (1.06–1.29), physical activity 1.13 (1.06–1.17), unhealthy diet 1.08 (1.04–1.13), and counseling according to highest prioritized level of advice stated by national guidelines 1.14 (1.09–1.18). Differences in comorbidities between men and women were found, with higher frequencies of coronary heart disease, congestive heart failure, atrial fibrillation, stroke, diabetes, and gout among men. Regarding antihypertensive treatment, women received less treatment of calcium channel blockers and ACE inhibitors, but more of angiotensin receptor blockers. Conclusions These findings highlight the need for targeted preventive efforts in PHC, especially for male patients, to address disparities in cardiovascular health outcomes. Small differences in preventive measures between public and privately run PHC suggest generally consistent care across healthcare ownership models.

Background: Many individuals with hypertension remain undiagnosed. We aimed to develop a predictive model for hypertension using diagnostic codes from prevailing electronic medical records in Swedish primary care. Methods: This sex- and age-matched case-control (1:5) study included patients aged 30–65 years living in the Stockholm Region, Sweden, with a newly recorded diagnosis of hypertension during 2010–19 (cases) and individuals without a recorded hypertension diagnosis during 2010–19 (controls), in total 507,618 individuals. Patients with diagnoses of cardiovascular diseases or diabetes were excluded. A stochastic gradient boosting machine learning model was constructed using the 1,309 most registered ICD-10 codes from primary care for three years prior the hypertension diagnosis. Results: The model showed an area under the curve (95 % confidence interval) of 0.748 (0.742–0.753) for females and 0.745 (0.740–0.751) for males for predicting diagnosis of hypertension within three years. The sensitivity was 63 % and 68 %, and the specificity 76 % and 73 %, for females and males, respectively. The 25 diagnoses that contributed the most to the model for females and males all exhibited a normalized relative influence >1 %. The codes contributing most to the model, all with an odds ratio of marginal effects >1 for both sexes, were dyslipidaemia, obesity, and encountering health services in other circumstances. Conclusions: This machine learning model, using prevailing recorded diagnoses within primary health care, may contribute to the identification of patients at risk of unrecognized hypertension. The added value of this predictive model beyond information of blood pressure warrants further study.

Abstract Background The prevention of type 2 diabetes is challenging due to the variable effects of risk factors at an individual level. Data-driven methods could be useful to detect more homogeneous groups based on risk factor variability. The aim of this study was to derive characteristic phenotypes using cluster analysis of common risk factors and to assess their utility to stratify the risk of type 2 diabetes. Methods Data on 7317 diabetes-free adults from Sweden were used in the main analysis and on 2332 diabetes-free adults from Mexico for external validation. Clusters were based on sex, family history of diabetes, educational attainment, fasting blood glucose and insulin levels, estimated insulin resistance and β-cell function, systolic and diastolic blood pressure, and BMI. The risk of type 2 diabetes was assessed using Cox proportional hazards models. The predictive accuracy and long-term stability of the clusters were then compared to different definitions of prediabetes. Results Six risk phenotypes were identified independently in both cohorts: very low-risk (VLR), low-risk low β-cell function (LRLB), low-risk high β-cell function (LRHB), high-risk high blood pressure (HRHBP), high-risk β-cell failure (HRBF), and high-risk insulin-resistant (HRIR). Compared to the LRHB cluster, the VLR and LRLB clusters showed a lower risk, while the HRHBP, HRBF, and HRIR clusters showed a higher risk of developing type 2 diabetes. The high-risk clusters, as a group, had a better predictive accuracy than prediabetes and adequate stability after 20 years. Conclusions Phenotypes derived using cluster analysis were useful in stratifying the risk of type 2 diabetes among diabetes-free adults in two independent cohorts. These results could be used to develop more precise public health interventions.

Aims: To study differences in cardiovascular prevention and hypertension management in primary care in men and women, with comparisons between public and privately operated primary health care (PHC). Methods: We used register data from Region Stockholm on collected prescribed medication and registered diagnoses, to identify patients aged 30 years and above with hypertension. Age-adjusted logistic regression was used to calculate odds ratios (ORs) with 99% confidence intervals (99% CIs) using public PHC centers as referents. Results: In total, 119,267 patients with a registered hypertension diagnosis at their primary care center were included; 58,239 men and 61,028 women. In terms of co-morbidities and medications, there were some differences between privately and publicly run PHC: registered diagnosis of dementia, which was higher at private PHC, age-adjusted OR 1.80 (1.24–2.69). For lifestyle counseling, privately run PHC had a higher rate of registered counseling for tobacco 1.17 (1.06–1.29), physical activity 1.13 (1.06–1.17), unhealthy diet 1.08 (1.04–1.13), and counseling according to highest prioritized level of advice stated by national guidelines 1.14 (1.09–1.18). Differences in comorbidities between men and women were found, with higher frequencies of coronary heart disease, congestive heart failure, atrial fibrillation, stroke, diabetes, and gout among men. Regarding antihypertensive treatment, women received less treatment of calcium channel blockers and ACE inhibitors, but more of angiotensin receptor blockers. Conclusions: These findings highlight the need for targeted preventive efforts in PHC, especially for male patients, to address disparities in cardiovascular health outcomes. Small differences in preventive measures between public and privately run PHC suggest generally consistent care across healthcare ownership models. [ABSTRACT FROM AUTHOR]

BackgroundMethotrexate (MTX) is the first line treatment for rheumatoid arthritis (RA), but failure of satisfying treatment response occurs in a significant proportion of patients. Here we present a longitudinal multi-omics study aimed at detecting molecular and cellular processes in peripheral blood associated with a successful methotrexate treatment of rheumatoid arthritis.MethodsEighty newly diagnosed patients with RA underwent clinical assessment and donated blood before initiation of MTX, and 3 months into treatment. Flow cytometry was used to describe cell types and presence of activation markers in peripheral blood, the expression of 51 proteins was measured in serum or plasma, and RNA sequencing was performed in peripheral blood mononuclear cells (PBMC). Response to treatment after 3 months was determined using the EULAR response criteria. We assessed the changes in biological phenotypes during treatment, and whether these changes differed between responders and non-responders with regression analysis. By using measurements from baseline, we also tried to find biomarkers of future MTX response or, alternatively, to predict MTX response.ResultsAmong the MTX responders, (Good or Moderate according to EULAR treatment response classification, n = 60, 75%), we observed changes in 29 partly overlapping cell types proportions, levels of 13 proteins and expression of 38 genes during treatment. These changes were in most cases suppressions that were stronger among responders compared to non-responders. Within responders to treatment, we observed a suppression of FOXP3 gene expression, reduction of immunoglobulin gene expression and suppression of genes involved in cell proliferation. The proportion of many HLA-DR expressing T-cell populations were suppressed in all patients irrespective of clinical response, and the proportion of many IL21R+ T-cells were reduced exclusively in non-responders. Using only the baseline measurements we could not detect any biomarkers or prediction models that could predict response to MTX.ConclusionWe conclude that a deep molecular and cellular phenotyping of peripheral blood cells in RA patients treated with methotrexate can reveal previously not recognized differences between responders and non-responders during 3 months of treatment with MTX. This may contribute to the understanding of MTX mode of action and explain non-responsiveness to MTX therapy.

Background Asymptomatic patients with meningiomas are increasingly detected, where management can be challenging in terms of surgery versus watchful waiting. Health-related quality of life (HRQoL) is an important factor in clinical decision-making, albeit not greatly studied in this patient group. The aim of this paper is to map the HRQoL among patients with surgically removed asymptomatic meningioma as compared to the general population. Methods Patients with first-time surgically treated asymptomatic meningioma between 2007 and 2013 were identified. Patients were invited in 2017 to answer a survey regarding different aspects of quality of life, using EuroQoL (EQ)-5D-3L, perceived health, lifestyle, and occupancy. Data from electronic patient records was obtained. The patients were matched based on age and gender with data from the Stockholm Region Public Health Cohort database. Results There was no difference in EQ-5D-3L or visual analog scale between the patients and their matched controls. Patients and controls experienced ill health to the same extent, but patients felt to a greater extent that this impacted their way of life. In 36% of patients, preoperative occupation was not resumed, mostly due to cognitive symptoms. Additionally, the study suggested social detachment in this cohort, as significantly more patients were living alone and had less emotional support compared to controls. Conclusions Although surgically treated patients with asymptomatic intracranial meningioma have similar overall HRQoL compared to the general population, surgery has an impact on return to work and cognitive function. [ABSTRACT FROM AUTHOR]

Abstract Background Rheumatoid arthritis (RA) is classified as seropositive or seronegative, depending on the presence/absence of rheumatoid factor (RF), primarily IgM RF, and/or anti-citrullinated protein antibodies (ACPA), commonly detected using anti-cyclic citrullinated peptide (CCP) assays. Known risk factors associate with the more severe seropositive form of RA; less is known about seronegative RA. Here, we examine risk factors and clinical phenotypes in relation to presence of autoantibodies in the RA subset that is traditionally defined as seronegative. Methods Anti-CCP2 IgG, 19 ACPA fine-specificities, IgM/IgG/IgA RF, anti-carbamylated-protein (CarP) antibodies, and 17 other autoantibodies, were analysed in 2755 RA patients and 370 controls. Antibody prevalence, levels, and co-occurrence were examined, and associations with risk factors and disease activity during 5 years were investigated for different antibody-defined RA subsets. Results Autoantibodies were detected in a substantial proportion of the traditionally defined seronegative RA subset, with ACPA fine-specificities found in 30%, IgA/IgG RF in 9.4%, and anti-CarP antibodies in 16%, with a 9.6% co-occurrence of at least two types of RA-associated autoantibodies. HLA-DRB1 shared epitope (SE) associated with the presence of ACPA in anti-CCP2-negative RA; in anti-CCP2-positive RA, the SE association was defined by six ACPA fine-specificities with high co-occurrence. Smoking associated with RF, but not with ACPA, in anti-CCP2-negative RA. Presence of ACPA and RF, but not anti-CarP antibodies, in conventionally defined “seronegative” RA, associated with worse clinical outcome. Conclusions “Seronegative” RA is not truly a seronegative disease subset. Additional screening for ACPA fine-specificities and IgA/IgG RF defines a group of patients that resembles seropositive patients with respect to risk factors and clinical picture and may contribute to earlier diagnosis for a subset of anti-CCP2−/IgM RF− patients with a high need for active treatment.

Based on the epidemiological link between periodontitis and rheumatoid arthritis (RA), and the unique feature of the periodontal bacterium Porphyromonas gingivalis to citrullinate proteins, it has been suggested that production of anti-citrullinated protein antibodies (ACPA), which are present in a majority of RA patients, may be triggered in the gum mucosa. To address this hypothesis, we investigated the antibody response to a citrullinated P. gingivalis peptide in relation to the autoimmune ACPA response in early RA, and examined citrulline-reactivity in monoclonal antibodies derived from human gingival B cells. Antibodies to a citrullinated peptide derived from P. gingivalis (denoted CPP3) and human citrullinated peptides were analyzed by multiplex array in 2,807 RA patients and 372 controls; associations with RA risk factors and clinical features were examined. B cells from inflamed gingival tissue were single-cell sorted, and immunoglobulin (Ig) genes were amplified, sequenced, cloned and expressed (n=63) as recombinant monoclonal antibodies, and assayed for citrulline-reactivities by enzyme-linked immunosorbent assay. Additionally, affinity-purified polyclonal anti-cyclic-citrullinated peptide (CCP2) IgG, and monoclonal antibodies derived from RA blood and synovial fluid B cells (n=175), were screened for CPP3-reactivity. Elevated anti-CPP3 antibody levels were detected in RA (11%), mainly CCP2+ RA, compared to controls (2%), p

Understanding the genetic background of complex diseases requires the expansion of studies beyond univariate associations. Therefore, it is important to use interaction assessments of risk factors in order to discover whether, and how genetic risk variants act together on disease development. The principle of interaction analysis is to explore the magnitude of the combined effect of risk factors on disease causation. In this study, we use simulations to investigate different scenarios of causation to show how the magnitude of the effect of two risk factors interact. We mainly focus on the two most commonly used interaction models, the additive and multiplicative risk scales, since there is often confusion regarding their use and interpretation. Our results show that the combined effect is multiplicative when two risk factors are involved in the same chain of events, an interaction called synergism. Synergism is often described as a deviation from additivity, which is a broader term. Our results also confirm that it is often relevant to estimate additive effect relationships, because they correspond to independent risk factors at low disease prevalence. Importantly, we evaluate the threshold of more than two required risk factors for disease causation, called the multifactorial threshold model. We found a simple mathematical relationship (square root) between the threshold and an additive-to-multiplicative linear effect scale (AMLES), where 0 corresponds to an additive effect and 1 to a multiplicative. We propose AMLES as a metric that could be used to test different effects relationships at the same time, given that it can simultaneously reveal additive, multiplicative and intermediate risk effects relationships. Finally, the utility of our simulation study was demonstrated using real data by analyzing and interpreting gene-gene interaction odds ratios from a rheumatoid arthritis case-control cohort.

Abstract OBJECTIVE: In rheumatoid arthritis (RA) several recent efforts have sought to discover means of predicting which patients would benefit from treatment. However, results have been discrepant with few successful replications. Our objective was to build a biobank with DNA, RNA and protein measurements to test the claim that the current state-of-the-art precision medicine will benefit RA patients. METHODS: We collected 451 blood samples from 61 healthy individuals and 185 RA patients initiating treatment, before treatment initiation and at a 3 month follow-up time. All samples were subjected to high-throughput RNA sequencing, DNA genotyping, extensive proteomics and flow cytometry measurements, as well as comprehensive clinical phenotyping. Literature review identified 2 proteins, 52 single-nucleotide polymorphisms (SNPs) and 72 gene-expression biomarkers that had previously been proposed as predictors of Tumor Necrosis Factor (TNF) inhibitor response (ΔDAS28-CRP), RESULTS: From these published TNFi biomarkers we found that 2 protein, 2 SNP and 8 mRNA biomarkers could be replicated in the 59 TNF initiating patients. Combining these replicated biomarkers into a single signature we found that we could explain 51% of the variation in ΔDAS28-CRP. This corresponds to a sensitivity of 0.73 and specificity of 0.78 for the prediction of three month ΔDAS28-CRP better than −1.2. CONCLUSIONS: The COMBINE biobank is currently the largest collection of multi-omics data from RA patients with high potential for discovery and replication. Taking advantage of this we surveyed the current state-of-the-art of drug-response stratification in RA, and identified a small set of previously published biomarkers available in peripheral blood which predicts clinical response to TNF blockade in this independent cohort.

The aetiology of multiple sclerosis (MS), an autoimmune demyelinating disease of the central nervous system (CNS), includes both genetic and environmental factors, but the pathogenesis is still incompletely known. We performed gene expression profiling on paired cerebrospinal fluid (CSF) and peripheral blood mononuclear cells (PBMCs) samples from 26 MS patients without immunomodulatory treatment, sampled in relapse or remission, and 18 controls using Human Genome U133 plus 2.0 arrays (Affymetrix). In the CSF, 939 probe sets detected differential expression in MS patients compared to controls, but none in PBMCs, confirming that CSF cells might mirror the disease processes. The regulation of selected transcripts in CSF of MS patients was confirmed by quantitative PCR. Unexpectedly however, when comparing MS patients in relapse to those in remission, 266 probe sets detected differential expression in PBMCs, but not in CSF cells, indicating the importance of events outside of the CNS in the triggering of relapse.