The predictive value of cardiovascular outcomes and mortality assessed by the C-reactive protein to albumin ratio in the UK Biobank.

BACKGROUND: The C-reactive protein/albumin ratio (CAR) seems to mirror disease severity and prognosis in several acute disorders particularly in elderly patients, yet less is known about if CAR is superior to C-reactive protein (CRP) in the general population. METHODS: Prospective study design on the UK Biobank, where serum samples of CRP and Albumin were used. Cox regression analyses were conducted to assess all-cause and cardiovascular mortality, myocardial infarction, ischemic stroke, and heart failure over a follow-up period of approximately 12.5 years. The Cox model was adjusted for established cardiovascular disease (CVD) risk factors, including age, sex, smoking habits, physical activity level, BMI level, systolic blood pressure, LDL-cholesterol, statin treatment, diabetes, and previous CVD, with hazard ratios (HRs) and corresponding 95% confidence intervals (CIs). Analyses were also stratified by sex, CRP level (< 10 and ≥ 10 mg/ml) and age (< 60 and ≥ 60 years). RESULTS: In total, 411,506 individuals (186,043 men and 225,463 women) were included. In comparisons between HRs for all adverse outcomes, the results were similar or identical for CAR and CRP. For example, both CAR and CRP, adjusted HRs for all-cause mortality were 1.13 (95% CI 1.12-1.14). Regarding CVD mortality, the adjusted HR for CAR was 1.14 (95% CI 1.12-1.15), while for CRP, it was 1.13 (95% CI 1.11-1.15). CONCLUSIONS: Within this study CAR was not superior to CRP in predictive ability of mortality or CVD disorders. CLINICAL TRIAL REGISTRATION NUMBER: Not applicable (cohort study).

ADAM-17 Activity and Its Relation to ACE2: Implications for Severe COVID-19.

There is a lack of studies aiming to assess cellular a disintegrin and metalloproteinase-17 (ADAM-17) activity in COVID-19 patients and the eventual associations with the shedding of membrane-bound angiotensin-converting enzyme 2 (mACE2). In addition, studies that investigate the relationship between ACE2 and ADAM-17 gene expressions in organs infected by SARS-CoV-2 are lacking. We used data from the Massachusetts general hospital COVID-19 study (306 COVID-19 patients and 78 symptomatic controls) to investigate the association between plasma levels of 33 different ADAM-17 substrates and COVID-19 severity and mortality. As a surrogate of cellular ADAM-17 activity, an ADAM-17 substrate score was calculated. The associations between soluble ACE2 (sACE2) and the ADAM-17 substrate score, renin, key inflammatory markers, and lung injury markers were investigated. Furthermore, we used data from the Genotype-Tissue Expression (GTEx) database to evaluate ADAM-17 and ACE2 gene expressions by age and sex in ages between 20-80 years. We found that increased ADAM-17 activity, as estimated by the ADAM-17 substrates score, was associated with COVID-19 severity (p = 0.001). ADAM-17 activity was also associated with increased mortality but did not reach statistical significance (p = 0.06). Soluble ACE2 showed the strongest positive correlation with the ADAM-17 substrate score, follow by renin, interleukin-6, and lung injury biomarkers. The ratio of ADAM-17 to ACE2 gene expression was highest in the lung. This study indicates that increased ADAM-17 activity is associated with severe COVID-19. Our findings also indicate that there may a bidirectional relationship between membrane-bound ACE2 shedding via increased ADAM-17 activity, dysregulated renin-angiotensin system (RAS) and immune signaling. Additionally, differences in ACE2 and ADAM-17 gene expressions between different tissues may be of importance in explaining why the lung is the organ most severely affected by COVID-19, but this requires further evaluation in prospective studies.

Relationships between Charlson comorbidity index associated biomarkers and outcomes among participants in the Malmö diet and cancer study.

INTRODUCTION: The aim was to evaluate two biomarker scores trained to identify comorbidity burden in the prediction of specified chronic morbidities, and mortality in the general population. METHODS: Cardiovascular biomarkers were measured in the cardiovascular cohort of the Malmö Diet and Cancer Study. A score of 19 biomarkers associated with Charlson Comorbidity Index (CCI) was created (BSMDC). Individuals with CCI diagnoses and other major comorbidities were excluded. Another score of 11 biomarkers associated with comorbidity burden from a previous study of acute dyspnea was also created (BSADYS). The scores were prospectively evaluated for prediction of mortality, and some chronic diseases, using Cox Proportional Hazards Model. RESULTS: Fully adjusted models showed that BSMDC was significantly associated per 1 SD increment of the score with incident COPD, 55%, and congestive heart failure, 32%; and with mortality, 33% cardiovascular, 91% respiratory, 30% cancer, and 45% with all-cause mortality. The BSADYS showed no association with these outcomes, after simultaneous inclusion of both biomarker scores to all the clinical covariates. CONCLUSION: BSMDC shows strong prediction of morbidity and mortality in individuals free from comorbidities at baseline, and the results suggest that healthy individuals with high level of BSMDC would benefit from intense preventive actions.

A score of 19 biomarkers associated with Charlson Comorbidity Index was created, the Biomarker Score of Malmö Diet and Cancer study (BSMDC).The created BSMDC index was associated with both incident COPD, and incident CHF.BSMDC was also associated with cardiovascular mortality, respiratory mortality, cancer mortality and with all-cause mortality.

A machine learning tool for identifying patients with newly diagnosed diabetes in primary care.

BACKGROUND AND AIM: It is crucial to identify a diabetes diagnosis early. Create a predictive model utilizing machine learning (ML) to identify new cases of diabetes in primary health care (PHC). METHODS: A case-control study utilizing data on PHC visits for sex-, age, and PHC-matched controls. Stochastic gradient boosting was used to construct a model for predicting cases of diabetes based on diagnostic codes from PHC consultations during the year before index (diagnosis) date and number of consultations. Variable importance was estimated using the normalized relative influence (NRI) score. Risks of having diabetes were calculated using odds ratios of marginal effects (ORME). Four groups by age and sex were studied, age-groups 35-64 years and ≥ 65 years in men and women, respectively. RESULTS: The most important predictive factors were hypertension with NRI 21.4-29.7 %, and obesity 4.8-15.2 %. The NRI for other top ten diagnoses and administrative codes generally ranged 1.0-4.2 %. CONCLUSIONS: Our data confirm the known risk patterns for predicting a new diagnosis of diabetes, and the need to test blood glucose frequently. To assess the full potential of ML for risk prediction purposes in clinical practice, future studies could include clinical data on life-style patterns, laboratory tests and prescribed medication.

Orthostatic hypotension is associated with higher levels of circulating endostatin.

Aims: The pathophysiology of orthostatic hypotension (OH), a common clinical condition, associated with adverse outcomes, is incompletely understood. We examined the relationship between OH and circulating endostatin, an endogenous angiogenesis inhibitor with antitumour effects proposed to be involved in blood pressure (BP) regulation. Methods and results: We compared endostatin levels in 146 patients with OH and 150 controls. A commercial chemiluminescence sandwich immunoassay was used to measure circulating levels of endostatin. Linear and multivariate logistic regressions were conducted to test the association between endostatin and OH. Endostatin levels were significantly higher in OH patients (59 024 ± 2513 pg/mL) vs. controls (44 090 ± 1978pg/mL, P < 0.001). A positive linear correlation existed between endostatin and the magnitude of systolic BP decline upon standing (P < 0.001). Using multivariate analysis, endostatin was associated with OH (adjusted odds ratio per 10% increase of endostatin in the whole study population = 1.264, 95% confidence interval 1.141-1.402), regardless of age, sex, prevalent cancer, and cardiovascular disease, as well as traditional cardiovascular risk factors. Conclusion: Circulating endostatin is elevated in patients with OH and may serve as a potential clinical marker of increased cardiovascular risk in patients with OH. Our findings call for external validation. Further research is warranted to clarify the underlying pathophysiological mechanisms.

Predicting new cases of hypertension in Swedish primary care with a machine learning tool.

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.