| 1. |
- Ahmad, Abrar, et al.
(författare)
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Precision prognostics for cardiovascular disease in Type 2 diabetes : a systematic review and meta-analysis
- 2024
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Ingår i: Communications medicine. - 2730-664X. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with Type 2 diabetes (T2D).METHODS: We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies.RESULTS: Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort.CONCLUSIONS: Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.
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| 2. |
- Bäck, Sophia, et al.
(författare)
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Assessment of transmitral and left atrial appendage flow rate from cardiac 4D-CT
- 2023
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Ingår i: Communications Medicine. - : Springer Nature. - 2730-664X. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Plain language summaryAssessing the blood flow inside the heart is important in diagnosis and treatment of various cardiovascular diseases, such as atrial fibrillation or heart failure. We developed a method to accurately track the motion of the heart walls over the course of a heartbeat in three-dimensional Computed Tomography (CT) images. Based on the motion, we calculated the amount of blood passing through the mitral valve and the left atrial appendage orifice, which are markers used in the diagnostic of heart failure and assessment of stroke risk in atrial fibrillation. The results agreed well with measurements from 4D flow MRI, an imaging technique that measures blood velocities. Our method could broaden the use of CT and make additional exams redundant. It can even be used to calculate the blood flow inside the heart. BackgroundCardiac time-resolved CT (4D-CT) acquisitions provide high quality anatomical images of the heart. However, some cardiac diseases require assessment of blood flow in the heart. Diastolic dysfunction, for instance, is diagnosed by measuring the flow through the mitral valve (MV), while in atrial fibrillation, the flow through the left atrial appendage (LAA) indicates the risk for thrombus formation. Accurate validated techniques to extract this information from 4D-CT have been lacking, however.MethodsTo measure the flow rate though the MV and the LAA from 4D-CT, we developed a motion tracking algorithm that performs a nonrigid deformation of the surface separating the blood pool from the myocardium. To improve the tracking of the LAA, this region was deformed separately from the left atrium and left ventricle. We compared the CT based flow with 4D flow and short axis MRI data from the same individual in 9 patients.ResultsFor the mitral valve flow, good agreement was found for the time span between the early and late diastolic peak flow (bias: <0.1 s). The ventricular stroke volume is similar compared to short-axis MRI (bias 3 ml). There are larger differences in the diastolic peak flow rates, with a larger bias for the early flow rate than the late flow rate. The peak LAA outflow rate measured with both modalities matches well (bias: -6 ml/s).ConclusionsOverall, the developed algorithm provides accurate tracking of dynamic cardiac geometries resulting in similar flow rates at the MV and LAA compared to 4D flow MRI. Back et al. describe a motion tracking algorithm to measure the flow rate through the mitral valve (MV) and the left atrial appendage (LAA) from 4D-CT data. The developed algorithm provided accurate tracking of dynamic cardiac geometries resulting in similar flow rates at the MV and LAA to those measured by 4D flow MRI.
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| 3. |
- Carson, Richard T., et al.
(författare)
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Perceptions of the seriousness of major public health problems during the COVID-19 pandemic in seven middle-income countries
- 2023
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Ingår i: Communications Medicine. - : Springer Nature. - 2730-664X. ; 3
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Tidskriftsartikel (refereegranskat)abstract
- IntroductionPublic perception of the seriousness of the COVID-19 pandemic compared to six other major public health problems (alcoholism and drug use, HIV/AIDS, malaria, tuberculosis, lung cancer and respiratory diseases caused by air pollution and smoking, and water-borne diseases like diarrhea) is unclear. We designed a survey to examine this issue using YouGov’s internet panels in seven middle-income countries in Africa, Asia, and Latin America in early 2022.MethodsRespondents rank ordered the seriousness of the seven health problems using a repeated best-worst question format. Rank-ordered logit models allow comparisons within and across countries and assessment of covariates.ResultsIn six of the seven countries, respondents perceived other respiratory illnesses to be a more serious problem than COVID-19. Only in Vietnam was COVID-19 ranked above other respiratory illnesses. Alcoholism and drug use was ranked the second most serious problem in the African countries. HIV/AIDS ranked relatively high in all countries. Covariates, particularly a COVID-19 knowledge scale, explained differences within countries; statistics about the pandemic were highly correlated with differences in COVID-19’s perceived seriousness.ConclusionsPeople in the seven middle-income countries perceived COVID-19 to be serious (on par with HIV/AIDS) but not as serious as other respiratory illnesses. In the African countries, respondents perceived alcoholism and drug use as more serious than COVID-19. Our survey-based approach can be used to quickly understand how the threat of a newly emergent disease, like COVID-19, fits into the larger context of public perceptions of the seriousness of health problems.
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| 4. |
- Castellano Ontiveros, Rodrigo, et al.
(författare)
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A machine learning-based approach for constructing remote photoplethysmogram signals from video cameras
- 2024
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Ingår i: Communications Medicine. - : Springer Nature. - 2730-664X. ; 4:1
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Tidskriftsartikel (refereegranskat)abstract
- Background Advancements in health monitoring technologies are increasingly relying on capturing heart signals from video, a method known as remote photoplethysmography (rPPG). This study aims to enhance the accuracy of rPPG signals using a novel computer technique.Methods We developed a machine-learning model to improve the clarity and accuracy of rPPG signals by comparing them with traditional photoplethysmogram (PPG) signals from sensors. The model was evaluated across various datasets and under different conditions, such as rest and movement. Evaluation metrics, including dynamic time warping (to assess timing alignment between rPPG and PPG) and correlation coefficients (to measure the linear association between rPPG and PPG), provided a robust framework for validating the effectiveness of our model in capturing and replicating physiological signals from videos accurately.Results Our method showed significant improvements in the accuracy of heart signals captured from video, as evidenced by dynamic time warping and correlation coefficients. The model performed exceptionally well, demonstrating its effectiveness in achieving accuracy comparable to direct-contact heart signal measurements.Conclusions This study introduces a novel and effective machine-learning approach for improving the detection of heart signals from video. The results demonstrate the flexibility of our method across various scenarios and its potential to enhance the accuracy of health monitoring applications, making it a promising tool for remote healthcare. This research explores a new way to monitor health using video, which is less invasive than traditional methods that require direct skin contact. We developed a computer program that improves the accuracy of heart signals captured from video. This is done by comparing these video-based signals with standard clinical signals from physical sensors on the skin. Our findings show that this new method can match the accuracy of conventional clinical methods, enhancing the reliability of non-contact health monitoring. This advancement could make health monitoring more accessible and comfortable, offering a potential for doctors to track patient health remotely, making everyday medical assessments easier and less intrusive. Ontiveros, Elgendi et al. devise and validate a machine learning approach that improves the quality of photoplethysmogram signals that can be obtained from video data. This ultimately contributes to advances in non-invasive health monitoring technologies.
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| 5. |
- De Neve, Jan-Walter, et al.
(författare)
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Relationship between adolescent anemia and school attendance observed during a nationally representative survey in India
- 2024
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Ingår i: Communications medicine. - 2730-664X. ; 4:1
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Anemia has been suggested to be related with schooling outcomes in India. Less is known, however, about whether the observed relationship persists after accounting for all household-level factors which may confound the association between anemia and schooling.METHODS: Nationally representative data on adolescents aged 15-18 years with data on measured hemoglobin level and school attendance were extracted from India's National Family Health Surveys conducted between 2005 and 2021. We compared school attendance between adolescents living in the same household but with varying levels of hemoglobin concentration, while controlling for age and period effects. We assessed heterogeneity in the relationship between anemia and school attendance across anemia severity groups and socio-demographic characteristics.RESULTS: The proportion of adolescents with any anemia is 55.2% (95% CI: 55.0-55.5) among young women and 31.0% (95% CI: 30.6-31.5) among young men. In conventional (between-household) regression models, having any anemia is associated with a 2.5 percentage point reduction (95% CI: 2.1-2.8) in school attendance; however, in household fixed-effects models, anemia has qualitatively small and non-significant effects on school attendance. Our results are consistent using alternative model specifications as well as across anemia severity groups, genders, types of relationship to the household head, household wealth quintiles, and states and union territories in India.CONCLUSIONS: This within-household analysis finds little evidence that anemia is associated with school attendance among adolescents in India. Observational studies likely overstate the connection between anemia and school attendance due to household factors that have not been accounted for.
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| 6. |
- Enroth, Stefan, 1976-, et al.
(författare)
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Data-driven analysis of a validated risk score for ovarian cancer identifies clinically distinct patterns during follow-up and treatment
- 2022
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Ingår i: Communications Medicine. - : Springer Nature. - 2730-664X. ; 2:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundOvarian cancer is the eighth most common cancer among women and due to late detection prognosis is poor with an overall 5-year survival of 30–50%. Novel biomarkers are needed to reduce diagnostic surgery and enable detection of early-stage cancer by population screening. We have previously developed a risk score based on an 11-biomarker plasma protein assay to distinguish benign tumors (cysts) from malignant ovarian cancer in women with adnexal ovarian mass.MethodsProtein concentrations of 11 proteins were characterized in plasma from 1120 clinical samples with a custom version of the proximity extension assay. The performance of the assay was evaluated in terms of prediction accuracy based on receiver operating characteristics (ROC) and multiple hypothesis adjusted Fisher’s Exact tests on achieved sensitivity and specificity.ResultsThe assay’s performance is validated in two independent clinical cohorts with a sensitivity of 0.83/0.91 and specificity of 0.88/0.92. We also show that the risk score follows the clinical development and is reduced upon treatment, and increased with relapse and cancer progression. Data-driven modeling of the risk score patterns during a 2-year follow-up after diagnosis identifies four separate risk score trajectories linked to clinical development and survival. A Cox proportional hazard regression analysis of 5-year survival shows that at time of diagnosis the risk score is the second-strongest predictive variable for survival after tumor stage, whereas MUCIN-16 (CA-125) alone is not significantly predictive.ConclusionThe robust performance of the biomarker assay across clinical cohorts and the correlation with clinical development indicates its usefulness both in the diagnostic work-up of women with adnexal ovarian mass and for predicting their clinical course.
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| 7. |
- Fredolini, Claudia, et al.
(författare)
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Proteome profiling of home-sampled dried blood spots reveals proteins of SARS-CoV-2 infections
- 2024
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Ingår i: Communications Medicine. - : Springer Nature. - 2730-664X. ; 4:1
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Tidskriftsartikel (refereegranskat)abstract
- Background Self-sampling of dried blood spots (DBS) offers new routes to gather valuable health-related information from the general population. Yet, the utility of using deep proteome profiling from home-sampled DBS to obtain clinically relevant insights about SARS-CoV-2 infections remains largely unexplored.Methods Our study involved 228 individuals from the general Swedish population who used a volumetric DBS sampling device and completed questionnaires at home during spring 2020 and summer 2021. Using multi-analyte COVID-19 serology, we stratified the donors by their response phenotypes, divided them into three study sets, and analyzed 276 proteins by proximity extension assays (PEA). After normalizing the data to account for variances in layman-collected samples, we investigated the association of DBS proteomes with serology and self-reported information.Results Our three studies display highly consistent variance of protein levels and share associations of proteins with sex (e.g., MMP3) and age (e.g., GDF-15). Studying seropositive (IgG+) and seronegative (IgG-) donors from the first pandemic wave reveals a network of proteins reflecting immunity, inflammation, coagulation, and stress response. A comparison of the early-infection phase (IgM+IgG-) with the post-infection phase (IgM-IgG+) indicates several proteins from the respiratory system. In DBS from the later pandemic wave, we find that levels of a virus receptor on B-cells differ between seropositive (IgG+) and seronegative (IgG-) donors.Conclusions Proteome analysis of volumetric self-sampled DBS facilitates precise analysis of clinically relevant proteins, including those secreted into the circulation or found on blood cells, augmenting previous COVID-19 reports with clinical blood collections. Our population surveys support the usefulness of DBS, underscoring the role of timing the sample collection to complement clinical and precision health monitoring initiatives. The COVID-19 pandemic has posed multiple challenges to healthcare systems. A significant gap that remains is a lack of understanding of the impact of SARS-CoV-2 on individuals who did not seek or require hospitalization. To address this, we distribute self-sampling devices to random citizens, aiming to analyze how blood protein levels are affected in people who have had COVID-19 but had no or mild symptoms. Conducting multiple molecular measurements in dried blood, our study confirms clinically known markers and their relationship to infection stages, even if the donors themselves collect the sample. Our work highlights the potential of combining self-sampling with laboratory methods to provide useful information on human health. This convenient patient-centric sampling approach may potentially be useful when studying other diseases. Fredolini et al. present a proteomics analysis of home-sampled dried blood spots taken from the general population in Stockholm during the COVID-19 pandemic. The study provides insights into the molecular effects of SARS-CoV-2 infection in non-hospitalized individuals and demonstrates the compatibility of self-sampled blood spots with proteomics.
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| 8. |
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| 9. |
- Helldén, Daniel, et al.
(författare)
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A stakeholder group assessment of interactions between child health and the sustainable development goals in Cambodia
- 2022
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Ingår i: Communications Medicine. - : Springer Nature. - 2730-664X. ; 2:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: With the implementation of the Sustainable Development Goals, a systematic assessment of how the goals influence child health and vice versa has been lacking. We aimed to contribute to such an assessment by investigating the interactions between child health and the Sustainable Development Goals in Cambodia. Methods: Based on the SDG Synergies approach, 272 interactions between 16 Cambodian Sustainable Development Goals and child health were evaluated by an interdisciplinary Cambodian stakeholder group. From this a cross-impact matrix was derived and network analysis applied to determine first and second-order effects of the interactions with a focus on child health. Results: We show that with the exception of Cambodian Sustainable Development Goal 15 (life on land) the interactions are perceived to be synergistic between the child health and the Cambodian Sustainable Development Goals, and progress on Cambodian Sustainable Development Goal 16 (peace, justice and strong institutions) could have the largest potential to contribute to the achievement of the Cambodian Sustainable Development Goals, both when it comes to first and second-order interactions. Conclusions: In this stakeholder assessment, our findings provide novel insights on how complex relationships play out at the country level and highlight important synergies and trade-offs, vital for accelerating the work toward the betterment of child health and achieving the Sustainable Development Goals.
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| 10. |
- Holst, Anders, et al.
(författare)
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Identifying causal relationships of cancer treatment and long-term health effects among 5-year survivors of childhood cancer in Southern Sweden
- 2022
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Ingår i: Communications medicine. - : Springer Science and Business Media LLC. - 2730-664X. ; 2, s. 1-12
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Tidskriftsartikel (refereegranskat)abstract
- Background: Survivors of childhood cancer can develop adverse health events later in life. Infrequent occurrences and scarcity of structured information result in analytical and statistical challenges. Alternative statistical approaches are required to investigate the basis of late effects in smaller data sets.Methods: Here we describe sex-specific health care use, mortality and causal associations between primary diagnosis, treatment and outcomes in a small cohort ( n = 2315) of 5-year survivors of childhood cancer ( n = 2129) in southern Sweden and a control group ( n = 11,882; age-, sex- and region-matched from the general population). We developed a constraint-based method for causal inference based on Bayesian estimation of distributions, and used it to investigate health care use and causal associations between diagnoses, treatments and outcomes. Mortality was analyzed by the Kaplan-Meier method. Results: Our results confirm a significantly higher health care usage and premature mortality among childhood cancer survivors as compared to controls. The developed method for causal inference identifies 98 significant associations ( p < 0.0001) where most are well known ( n = 73; 74.5%). Hitherto undescribed associations are identified ( n = 5; 5.1%). These were between use of alkylating agents and eye conditions, topoisomerase inhibitors and viral infections; pituitary surgery and intestinal infections; and cervical cancer and endometritis. We discuss study-related biases ( n = 20; 20.4%) and limitations. Conclusions: The findings contribute to a broader understanding of the consequences of cancer treatment. The study shows relevance for small data sets and causal inference, and presents the method as a complement to traditional statistical approaches.
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