Data-driven comorbidity analysis of 100 common disorders reveals patient subgroups with differing mortality risks and laboratory correlates

Comorbidities confound metabolomics studies of human disease

The co-occurrence of multiple chronic conditions, termed multimorbidity, presents an expanding global health challenge, demanding effective diagnostics and treatment strategies. Chronic ailments such as obesity, diabetes, and cardiovascular diseases have been linked to metabolites interacting between the host and microbiota. In this study, we investigated the impact of co-existing conditions on risk estimations for 1375 plasma metabolites in 919 individuals from population-based Estonian Biobank cohort using liquid chromatography mass spectrometry (LC-MS) method. We leveraged annually linked national electronic health records (EHRs) data to delineate comorbidities in incident cases and controls for the 14 common chronic conditions. Among the 254 associations observed across 13 chronic conditions, we primarily identified disease-specific risk factors (92%, 217/235), with most predictors (93%, 219/235) found to be related to the gut microbiome upon cross-referencing recent literature data. Accounting for comorbidities led to a reduction of common metabolite predictors across various conditions. In conclusion, our study underscores the potential of utilizing biobank-linked retrospective and prospective EHRs for the disease-specific profiling of diverse multifactorial chronic conditions.

The Impact of Chronic Conditions on Productivity-Adjusted Life-Years in Both the Workplace and Household Settings in the General Adult Population in Finland

OBJECTIVES

This study aimed to quantify the burden of 8 noncommunicable conditions on productivity-adjusted life-years (PALYs) at work and within the household among the Finnish general adult population.

METHODS

Survey data on 18- to 79-year-old Finnish respondents collected in 2022 were used to calculate age- and sex-specific productivity indices at work and within the household using 0- and 1-inflated beta regression for individuals with and without a certain condition (asthma or chronic obstructive pulmonary disease, cardiovascular disease, depression or other mental health problem, diabetes, gastrointestinal disease, hypothyroidism or other thyroid disease, migraine or other chronic headache, and musculoskeletal disease). Age and sex distributions of the Finnish population obtained from Statistics Finland together with the prevalence of the condition and the estimated productivity indices were used to produce the population-level 1-year losses in PALYs at work and within the household.

RESULTS

Among 8 conditions, depression and other mental health problems had the highest PALY losses (99 570 PALY loss burden at work, 256 086 PALY loss at home, and 250 980 PALY loss in general adult populations), with diabetes having the lowest (3666 PALY loss burden at work, 46 344 PALY loss at home, and 43 443 PALY loss in general adult populations). All the examined conditions were as significant in affecting both the productivity at work and the within-household productivity.

CONCLUSIONS

Depression and other mental health problems have a major effect on self-reported work ability and productivity compared with other chronic conditions.

A comparative study of clinical trial and real-world data in patients with diabetic kidney disease

A growing body of research is focusing on real-world data (RWD) to supplement or replace randomized controlled trials (RCTs). However, due to the disparities in data generation mechanisms, differences are likely and necessitate scrutiny to validate the merging of these datasets. We compared the characteristics of RCT data from 5734 diabetic kidney disease patients with corresponding RWD from electronic health records (EHRs) of 23,523 patients. Demographics, diagnoses, medications, laboratory measurements, and vital signs were analyzed using visualization, statistical comparison, and cluster analysis. RCT and RWD sets exhibited significant differences in prevalence, longitudinality, completeness, and sampling density. The cluster analysis revealed distinct patient subgroups within both RCT and RWD sets, as well as clusters containing patients from both sets. We stress the importance of validation to verify the feasibility of combining RCT and RWD, for instance, in building an external control arm. Our results highlight general differences between RCT and RWD sets, which should be considered during the planning stages of an RCT-RWD study. If they are, RWD has the potential to enrich RCT data by providing first-hand baseline data, filling in missing data or by subgrouping or matching individuals, which calls for advanced methods to mitigate the differences between datasets.

Natural Language Processing for Drug Discovery Knowledge Graphs: Promises and Pitfalls

Building and analyzing knowledge graphs (KGs) to aid drug discovery is a topical area of research. A salient feature of KGs is their ability to combine many heterogeneous data sources in a format that facilitates discovering connections. The utility of KGs has been exemplified in areas such as drug repurposing, with insights made through manual exploration and modeling of the data. In this chapter, we discuss promises and pitfalls of using natural language processing (NLP) to mine "unstructured text"- typically from scientific literature- as a data source for KGs. This draws on our experience of initially parsing "structured" data sources-such as ChEMBL-as the basis for data within a KG, and then enriching or expanding upon them using NLP. The fundamental promise of NLP for KGs is the automated extraction of data from millions of documents-a task practically impossible to do via human curation alone. However, there are many potential pitfalls in NLP-KG pipelines, such as incorrect named entity recognition and ontology linking, all of which could ultimately lead to erroneous inferences and conclusions.

How much disease risk is due to old age and established risk factors?

Improved healthcare is leading to older populations and increasing numbers of individuals experiencing multiple diseases, possibly concurrently (multimorbidity). This article asks whether the observed number of new diseases is more than expected based on age and established risk factors alone, assuming that disease risk is unchanged by prior or pre-existing disease. This is accomplished by designing a new epidemiological approach, where the expected number of disease types are estimated for individuals without prior disease, by combining individual risk predictions with a "Poisson-Binomial" model to estimate the expected number of new diseases and its confidence interval. For 123 diseases in men and 99 diseases in women, the expected number of new diseases based on age and established risk factors was approximately 2 / 3 of that observed, with the observed number of new diseases approximately 1.5 times that predicted. The differences could not be explained by natural statistical variation, and provide a rigorous statistical demonstration of lower disease risk for individuals without any previous disease. The multiple of 1.5 was sufficiently consistent across different diseases to prevent its use for classification of disease types, but there were differences for subgroups such as smokers with high body mass index, and for some classes of disease (as defined by the International Classification of Diseases, version 10). The results suggest that empirical modeling might allow reliable predictions of future hospital admissions, and confirm the value of conventional epidemiological approaches that study disease risk in healthy individuals. The implications and future possibilities of this new approach are discussed.