Relative contribution of comorbid diseases to health-related quality of life in patients with Parkinson's disease

BACKGROUND

Multimorbidity is common in elderly people, and one of the major consequences of multimorbidity is low health-related quality of life (HRQoL). The aim of this study was to investigate the frequency of comorbid diseases in patients with Parkinson's disease (PD) and to analyze their relative importance in HRQoL. The aim was also to examine agreement between the generic 15D questionnaire and the PD-specific Parkinson's Disease Questionnaire (PDQ-8) to further validate 15D in the evaluation of HRQoL in patients with PD.

METHODS

Patients with PD (N = 551) filled a questionnaire on comorbid diseases, and the 15D questionnaire yielding a 15-dimensional health profile and a score representing the overall HRQoL. Self-organizing map was used for an unsupervised pattern recognition of the health profiles. Relative importance analysis was used to evaluate the contribution of 16 comorbid diseases to the 15D score. The agreement between 15D and PDQ-8 questionnaires was studied in a subset of 81 patients that were examined clinically.

RESULTS

533 patients (96.7%) reported comorbid diseases. The most affected dimensions in the 15D questionnaire were secretion, usual activities, discomfort and symptoms, and sexual activity. Self-organizing map identified three patterns of health profiles that included patients with high, low or transition HRQoL. The transition subgroup was similar to low HRQoL subgroup in non-motor dimensions. Sixteen comorbid diseases explained 33.7% of the variance in the 15D score. Memory deficit, depression, heart failure, and atrial fibrillation had the highest relative importance. The intraclass correlation coefficient between the generic 15D and the PD-specific PDQ-8 was 0.642 suggesting moderate reliability.

CONCLUSIONS

The most marked differences in HRQoL were in the dimensions of secretion, usual activities, and sexual activity. Pattern detection of 15D health dimensions enabled the detection of a subgroup with disproportionately poor HRQoL in non-motor dimensions. The comorbid diseases affecting most to HRQoL were memory deficit and depression. The generic 15D questionnaire can be used in the evaluation of HRQoL in PD patients.

COVID-19 associated pulmonary aspergillosis in critically-ill patients: a prospective multicenter study in the era of Delta and Omicron variants

BACKGROUND

During the first COVID-19 pandemic wave, COVID-19-associated pulmonary aspergillosis (CAPA) has been reported in up to 11-28% of critically ill COVID-19 patients and associated with increased mortality. As new SARS-CoV-2 variants emerged, the characteristics of critically ill COVID-19 patients have evolved, particularly in the era of Omicron. The purpose of this study is to investigate the characteristics of CAPA in the era of new variants.

METHODS

This is a prospective multicenter observational cohort study conducted in France in 36 participating intensive care units (ICU), between December 7th, 2021 and April 26th 2023. Diagnosis criteria of CAPA relied on European Confederation of Medical Mycology (ECMM)/International Society for Human & Animal Mycology (ISHAM) consensus criteria.

RESULTS

566 patients were included over the study period. The prevalence of CAPA was 5.1% [95% CI 3.4-7.3], and rose to 9.1% among patients who required invasive mechanical ventilation (IMV). Univariable analysis showed that CAPA patients were more frequently immunosuppressed and required more frequently IMV support, vasopressors and renal replacement therapy during ICU stay than non-CAPA patients. SAPS II score at ICU admission, immunosuppression, and a SARS-CoV-2 Delta variant were independently associated with CAPA in multivariable logistic regression analysis. Although CAPA was not significantly associated with day-28 mortality, patients with CAPA experienced a longer duration of mechanical ventilation and ICU stay.

CONCLUSION

This study contributes valuable insights into the prevalence, characteristics, and outcomes of CAPA in the era of Delta and Omicron variants. We report a lower prevalence of CAPA (5.1%) among critically-ill COVID-19 patients than previously reported, mainly affecting intubated-patients. Duration of mechanical ventilation and ICU stay were significantly longer in CAPA patients.

Medication profiling in women with type 1 diabetes highlights the importance of adequate, guideline-based treatment in low-risk groups

Effective treatment may prevent kidney complications, but women might be underprescribed. Novel, data-driven insights into prescriptions and their relationship with kidney health in women with type 1 diabetes may help to optimize treatment. We identified six medication profiles in 1164 women from the FinnDiane Study with normal albumin excretion rate based on clusters of their baseline prescription data using a self-organizing map. Future rapid kidney function decline was defined as an annual estimated glomerular filtration rate (eGFR) loss > 3 ml/min/1.73 m2 after baseline. Two profiles were associated with future decline: Profile ARB with the highest proportion of angiotensin receptor blockers (odds ratio [OR] 2.75, P = 0.02) and highly medicated women in profile HighMed (OR 2.55, P = 0.03). Compared with profile LowMed (low purchases of all), profile HighMed had worse clinical characteristics, whereas in profile ARB only systolic blood pressure was elevated. Importantly, the younger women in profile ARB with fewer kidney protective treatments developed a rapid decline despite otherwise similar baseline characteristics to profile ACE & Lipids (the highest proportions of ACE inhibitors and lipid-modifying agents) without a future rapid decline. In conclusion, medication profiles identified different future eGFR trajectories in women with type 1 diabetes revealing potential treatment gaps for younger women.

Longitudinal metabolomics of increasing body-mass index and waist-hip ratio reveals two dynamic patterns of obesity pandemic

BACKGROUND/OBJECTIVE

This observational study dissects the complex temporal associations between body-mass index (BMI), waist-hip ratio (WHR) and circulating metabolomics using a combination of longitudinal and cross-sectional population-based datasets and new systems epidemiology tools.

SUBJECTS/METHODS

Firstly, a data-driven subgrouping algorithm was employed to simplify high-dimensional metabolic profiling data into a single categorical variable: a self-organizing map (SOM) was created from 174 metabolic measures from cross-sectional surveys (FINRISK, n = 9708, ages 25-74) and a birth cohort (NFBC1966, n = 3117, age 31 at baseline, age 46 at follow-up) and an expert committee defined four subgroups of individuals based on visual inspection of the SOM. Secondly, the subgroups were compared regarding BMI and WHR trajectories in an independent longitudinal dataset: participants of the Young Finns Study (YFS, n = 1286, ages 24-39 at baseline, 10 years follow-up, three visits) were categorized into the four subgroups and subgroup-specific age-dependent trajectories of BMI, WHR and metabolic measures were modelled by linear regression.

RESULTS

The four subgroups were characterised at age 39 by high BMI, WHR and dyslipidemia (designated TG-rich); low BMI, WHR and favourable lipids (TG-poor); low lipids in general (Low lipid) and high low-density-lipoprotein cholesterol (High LDL-C). Trajectory modelling of the YFS dataset revealed a dynamic BMI divergence pattern: despite overlapping starting points at age 24, the subgroups diverged in BMI, fasting insulin (three-fold difference at age 49 between TG-rich and TG-poor) and insulin-associated measures such as triglyceride-cholesterol ratio. Trajectories also revealed a WHR progression pattern: despite different starting points at the age of 24 in WHR, LDL-C and cholesterol-associated measures, all subgroups exhibited similar rates of change in these measures, i.e. WHR progression was uniform regardless of the cross-sectional metabolic profile.

CONCLUSIONS

Age-associated weight variation in adults between 24 and 49 manifests as temporal divergence in BMI and uniform progression of WHR across metabolic health strata.

Metabolic profile-based subgroups can identify differences in brain volumes and brain iron deposition

AIMS

To evaluate associations of metabolic profiles and biomarkers with brain atrophy, lesions, and iron deposition to understand the early risk factors associated with dementia.

MATERIALS AND METHODS

Using data from 26 239 UK Biobank participants free from dementia and stroke, we assessed the associations of metabolic subgroups, derived using an artificial neural network approach (self-organizing map), and 39 individual biomarkers with brain MRI measures: total brain volume (TBV), grey matter volume (GMV), white matter volume (WMV), hippocampal volume (HV), white matter hyperintensity (WMH) volume, and caudate iron deposition.

RESULTS

In metabolic subgroup analyses, participants characterized by high triglycerides and liver enzymes showed the most adverse brain outcomes compared to the healthy reference subgroup with high-density lipoprotein cholesterol and low body mass index (BMI) including associations with GMV (βstandardized -0.20, 95% confidence interval [CI] -0.24 to -0.16), HV (βstandardized -0.09, 95% CI -0.13 to -0.04), WMH volume (βstandardized 0.22, 95% CI 0.18 to 0.26), and caudate iron deposition (βstandardized 0.30, 95% CI 0.25 to 0.34), with similar adverse associations for the subgroup with high BMI, C-reactive protein and cystatin C, and the subgroup with high blood pressure (BP) and apolipoprotein B. Among the biomarkers, striking associations were seen between basal metabolic rate (BMR) and caudate iron deposition (βstandardized 0.23, 95% CI 0.22 to 0.24 per 1 SD increase), GMV (βstandardized -0.15, 95% CI -0.16 to -0.14) and HV (βstandardized -0.11, 95% CI -0.12 to -0.10), and between BP and WMH volume (βstandardized 0.13, 95% CI 0.12 to 0.14 for diastolic BP).

CONCLUSIONS

Metabolic profiles were associated differentially with brain neuroimaging characteristics. Associations of BMR, BP and other individual biomarkers may provide insights into actionable mechanisms driving these brain associations.

Clinical phenotypes and outcomes associated with SARS-CoV-2 variant Omicron in critically ill French patients with COVID-19

Infection with SARS-CoV-2 variant Omicron is considered to be less severe than infection with variant Delta, with rarer occurrence of severe disease requiring intensive care. Little information is available on comorbid factors, clinical conditions and specific viral mutational patterns associated with the severity of variant Omicron infection. In this multicenter prospective cohort study, patients consecutively admitted for severe COVID-19 in 20 intensive care units in France between December 7th 2021 and May 1st 2022 were included. Among 259 patients, we show that the clinical phenotype of patients infected with variant Omicron (n = 148) is different from that in those infected with variant Delta (n = 111). We observe no significant relationship between Delta and Omicron variant lineages/sublineages and 28-day mortality (adjusted odds ratio [95% confidence interval] = 0.68 [0.35–1.32]; p = 0.253). Among Omicron-infected patients, 43.2% are immunocompromised, most of whom have received two doses of vaccine or more (85.9%) but display a poor humoral response to vaccination. The mortality rate of immunocompromised patients infected with variant Omicron is significantly higher than that of non-immunocompromised patients (46.9% vs 26.2%; p = 0.009). In patients infected with variant Omicron, there is no association between specific sublineages (BA.1/BA.1.1 (n = 109) and BA.2 (n = 21)) or any viral genome polymorphisms/mutational profile and 28-day mortality.

SARS-CoV-2 variant Omicron has been suggested to cause less severe disease. This prospective study shows that the clinical phenotype in patients infected with Omicron differs from patients infected with Delta but no association between Delta and Omicron including sublineages and mortality was observed.

Cross-sectional metabolic subgroups and 10-year follow-up of cardiometabolic multimorbidity in the UK Biobank

We assigned 329,908 UK Biobank participants into six subgroups based on a self-organizing map of 51 biochemical measures (blinded for clinical outcomes). The subgroup with the most favorable metabolic traits was chosen as the reference. Hazard ratios (HR) for incident disease were modeled by Cox regression. Enrichment ratios (ER) of incident multi-morbidity versus randomly expected co-occurrence were evaluated by permutation tests; ER is like HR but captures co-occurrence rather than event frequency. The subgroup with high urinary excretion without kidney stress (HR = 1.24) and the subgroup with the highest apolipoprotein B and blood pressure (HR = 1.52) were associated with ischemic heart disease (IHD). The subgroup with kidney stress, high adiposity and inflammation was associated with IHD (HR = 2.11), cancer (HR = 1.29), dementia (HR = 1.70) and mortality (HR = 2.12). The subgroup with high liver enzymes and triglycerides was at risk of diabetes (HR = 15.6). Multimorbidity was enriched in metabolically favorable subgroups (3.4 ≤ ER ≤ 4.0) despite lower disease burden overall; the relative risk of co-occurring disease was higher in the absence of obvious metabolic dysfunction. These results provide synergistic insight into metabolic health and its associations with cardiovascular disease in a large population sample.

Multi-Cohort Transcriptomic Subtyping of B-Cell Acute Lymphoblastic Leukemia

RNA sequencing provides a snapshot of the functional consequences of genomic lesions that drive acute lymphoblastic leukemia (ALL). The aims of this study were to elucidate diagnostic associations (via machine learning) between mRNA-seq profiles, independently verify ALL lesions and develop easy-to-interpret transcriptome-wide biomarkers for ALL subtyping in the clinical setting. A training dataset of 1279 ALL patients from six North American cohorts was used for developing machine learning models. Results were validated in 767 patients from Australia with a quality control dataset across 31 tissues from 1160 non-ALL donors. A novel batch correction method was introduced and applied to adjust for cohort differences. Out of 18,503 genes with usable expression, 11,830 (64%) were confounded by cohort effects and excluded. Six ALL subtypes (ETV6::RUNX1, KMT2A, DUX4, PAX5 P80R, TCF3::PBX1, ZNF384) that covered 32% of patients were robustly detected by mRNA-seq (positive predictive value ≥ 87%). Five other frequent subtypes (CRLF2, hypodiploid, hyperdiploid, PAX5 alterations and Ph-positive) were distinguishable in 40% of patients at lower accuracy (52% ≤ positive predictive value ≤ 73%). Based on these findings, we introduce the Allspice R package to predict ALL subtypes and driver genes from unadjusted mRNA-seq read counts as encountered in real-world settings. Two examples of Allspice applied to previously unseen ALL patient samples with atypical lesions are included.

Heterogeneity of Treatment Effects for Intensive Blood Pressure Therapy by Individual Components of FRS: An Unsupervised Data-Driven Subgroup Analysis in SPRINT and ACCORD

Background

Few studies have answered the guiding significance of individual components of the Framingham risk score (FRS) to the risk of cardiovascular disease (CVD) after antihypertensive treatment. This study on the systolic blood pressure intervention trial (SPRINT) and the Action to Control Cardiovascular Risk in Diabetes blood pressure trial (ACCORD-BP) aimed to reveal previously undetected association patterns between individual components of the FRS and heterogeneity of treatment effects (HTEs) of intensive blood pressure control.

Methods

A self-organizing map (SOM) methodology was applied to identify CVD-risk-specific subgroups in the SPRINT (n = 8,773), and the trained SOM was utilized directly in 4,495 patients from the ACCORD. The primary endpoints were myocardial infarction (MI), non-myocardial infarction acute coronary syndrome (non-MI ACS), stroke, heart failure (HF), death from CVD causes, and a primary composite cardiovascular outcome. Cox proportional hazards models were then used to explore the potential heterogeneous response to intensive SBP control.

Results

We identified four SOM-based subgroups with distinct individual components of FRS profiles and the CVD risk. For individuals with type 2 diabetes mellitus (T2DM) in the ACCORD or without diabetes in the SPRINT, subgroup I characterized by male with the lowest concentrations for total cholesterol (TC) and high-density lipoprotein (HDL) cholesterol measures, experienced the highest risk for major CVD. Conversely, subgroup III characterized by a female with the highest values for these measures represented as the lowest CVD risk. Furthermore, subgroup II, with the highest systolic blood pressure (SBP) and no antihypertensive agent use at baseline, had a significantly greater frequency of non-MI ACS under intensive BP control, the number needed to harm (NNH) was 84.24 to cause 1 non-MI ACS [absolute risk reduction (ARR) = −1.19%; 95% CI: −2.08, −0.29%] in the SPRINT [hazard ratio (HR) = 3.62; 95% CI: 1.33, 9.81; P = 0.012], and the NNH of was 43.19 to cause 1 non-MI ACS (ARR = −2.32%; 95% CI: −4.63, 0.00%) in the ACCORD (HR = 1.81; 95% CI: 1.01–3.25; P = 0.046). Finally, subgroup IV characterized by mostly younger patients with antihypertensive medication use and smoking history represented the lowest risk for stroke, HF, and relatively low risk for death from CVD causes and primary composite CVD outcome in SPRINT, however, except stroke, a low risk for others were not observed in ACCORD.

Conclusion

Similar findings in patients with hypertensive with T2DM or without diabetes by multivariate subgrouping suggested that the individual components of the FRS could enrich or improve CVD risk assessment. Further research was required to clarify the potential mechanism.

Assessing Cardiac Amyloidosis Subtypes by Unsupervised Phenotype Clustering Analysis

BACKGROUND

Cardiac amyloidosis (CA) is a set of amyloid diseases with usually predominant cardiac symptoms, including light-chain amyloidosis (AL), hereditary variant transthyretin amyloidosis (ATTRv), and wild-type transthyretin amyloidosis (ATTRwt). CA are characterized by high heterogeneity in phenotypes leading to diagnosis delay and worsened outcomes.

OBJECTIVES

The authors used clustering analysis to identify typical clinical profiles in a large population of patients with suspected CA.

METHODS

Data were collected from the French Referral Center for Cardiac Amyloidosis database (Hôpital Henri Mondor, Créteil), including 1,394 patients with suspected CA between 2010 and 2018: 345 (25%) had a diagnosis of AL, 263 (19%) ATTRv, 402 (29%) ATTRwt, and 384 (28%) no amyloidosis. Based on comprehensive clinicobiological phenotyping, unsupervised clustering analyses were performed by artificial neural network-based self-organizing maps to identify patient profiles (clusters) with similar characteristics, independent of the final diagnosis and prognosis.

RESULTS

Mean age and left ventricular ejection fraction were 72 ± 13 years and 52% ± 13%, respectively. The authors identified 7 clusters of patients with contrasting profiles and prognosis. AL patients were distinctively located within a typical cluster; ATTRv patients were distributed across 4 clusters with varying clinical presentations, 1 of which overlapped with patients without amyloidosis; interestingly, ATTRwt patients spread across 3 distinct clusters with contrasting risk factors, biological profiles, and prognosis.

CONCLUSIONS

Clustering analysis identified 7 clinical profiles with varying characteristics, prognosis, and associations with diagnosis. Especially in patients with ATTRwt, these results suggest key areas to improve amyloidosis diagnosis and stratify prognosis depending on associated risk factors.

Factors impacting self-management ability in patients with chronic diseases in the United Arab Emirates, 2019

Aim: Poor adherence to chronic disease therapy is a critical global problem that negatively effects the long-term therapy for chronic diseases, resulting in negative population health and economic effects. The WHO multidimensional model proposed a systems-based approach for improving adherence to chronic disease therapy. Patients & methods: In the current study, the WHO five-dimension framework was used to evaluate factors among, chronic-disease patients in the United Arab Emirates. Results: We show that patient's understanding of disease, involvement in treatment decision, age more than 40 years, time spent with physician and fear of how patients were perceived by others were the most predictive factors associated with a high ability to self-manage a chronic disease. Conclusion: Sociocultural factors have an indirect impact on disease self-management.

EpiMetal: an open-source graphical web browser tool for easy statistical analyses in epidemiology and metabolomics

MOTIVATION

An intuitive graphical interface that allows statistical analyses and visualizations of extensive data without any knowledge of dedicated statistical software or programming.

IMPLEMENTATION

EpiMetal is a single-page web application written in JavaScript, to be used via a modern desktop web browser.

GENERAL FEATURES

Standard epidemiological analyses and self-organizing maps for data-driven metabolic profiling are included. Multiple extensive datasets with an arbitrary number of continuous and category variables can be integrated with the software. Any snapshot of the analyses can be saved and shared with others via a www-link. We demonstrate the usage of EpiMetal using pilot data with over 500 quantitative molecular measures for each sample as well as in two large-scale epidemiological cohorts (N >10 000).

AVAILABILITY

The software usage exemplar and the pilot data are open access online at [http://EpiMetal.computationalmedicine.fi]. MIT licensed source code is available at the Github repository at [https://github.com/amergin/epimetal].

Data-driven multivariate population subgrouping via lipoprotein phenotypes versus apolipoprotein B in the risk assessment of coronary heart disease

BACKGROUND AND AIMS

Population subgrouping has been suggested as means to improve coronary heart disease (CHD) risk assessment. We explored here how unsupervised data-driven metabolic subgrouping, based on comprehensive lipoprotein subclass data, would work in large-scale population cohorts.

METHODS

We applied a self-organizing map (SOM) artificial intelligence methodology to define subgroups based on detailed lipoprotein profiles in a population-based cohort (n = 5789) and utilised the trained SOM in an independent cohort (n = 7607). We identified four SOM-based subgroups of individuals with distinct lipoprotein profiles and CHD risk and compared those to univariate subgrouping by apolipoprotein B quartiles.

RESULTS

The SOM-based subgroup with highest concentrations for non-HDL measures had the highest, and the subgroup with lowest concentrations, the lowest risk for CHD. However, apolipoprotein B quartiles produced better resolution of risk than the SOM-based subgroups and also striking dose-response behaviour.

CONCLUSIONS

These results suggest that the majority of lipoprotein-mediated CHD risk is explained by apolipoprotein B-containing lipoprotein particles. Therefore, even advanced multivariate subgrouping, with comprehensive data on lipoprotein metabolism, may not advance CHD risk assessment.

Perspectivas do uso de mineração de dados e aprendizado de máquina em saúde e segurança no trabalho

Resumo Introdução: a variedade, volume e velocidade de geração de dados (big data) possibilitam novas e mais complexas análises. Objetivo: discutir e apresentar técnicas de mineração de dados (data mining) e de aprendizado de máquina (machine learning) para auxiliar pesquisadores de Saúde e Segurança no Trabalho (SST) na escolha da técnica adequada para lidar com big data. Métodos: revisão bibliográfica com foco em data mining e no uso de análises preditivas com machine learning e suas aplicações para auxiliar diagnósticos e predição de riscos em SST. Resultados: a literatura indica que aplicações de data mining com algoritmos de machine learning para análises preditivas em saúde pública e em SST apresentam melhor desempenho em comparação com análises tradicionais. São sugeridas técnicas de acordo com o tipo de pesquisa almejada. Discussão: data mining tem se tornado uma alternativa cada vez mais comum para lidar com bancos de dados de saúde pública, possibilitando analisar grandes volumes de dados de morbidade e mortalidade. Tais técnicas não visam substituir o fator humano, mas auxiliar em processos de tomada de decisão, servir de ferramenta para a análise estatística e gerar conhecimento para subsidiar ações que possam melhorar a qualidade de vida do trabalhador.