Disease networks identify specific conditions and pleiotropy influencing multimorbidity in the general population

Abstract

Multimorbidity is an emerging topic in public health policy because of its increasing prevalence and socio-economic impact. However, the age- and gender-dependent trends of disease associations at fine resolution, and the underlying genetic factors, remain incompletely understood. Here, by analyzing disease networks from electronic medical records of primary health care, we identify key conditions and shared genetic factors influencing multimorbidity. Three types of diseases are outlined: “central”, which include chronic and non-chronic conditions, have higher cumulative risks of disease associations; “community roots” have lower cumulative risks, but inform on continuing clustered disease associations with age; and “seeds of bursts”, which most are chronic, reveal outbreaks of disease associations leading to multimorbidity. The diseases with a major impact on multimorbidity are caused by genes that occupy central positions in the network of human disease genes. Alteration of lipid metabolism connects breast cancer, diabetic neuropathy and nutritional anemia. Evaluation of key disease associations by a genome-wide association study identifies shared genetic factors and further supports causal commonalities between nervous system diseases and nutritional anemias. This study also reveals many shared genetic signals with other diseases. Collectively, our results depict novel population-based multimorbidity patterns, identify key diseases within them, and highlight pleiotropy influencing multimorbidity.

MorbiNet: multimorbidity networks in adult general population. Analysis of type 2 diabetes mellitus comorbidity

“…Cholecystitis/cholelithiasis and cataract have also a high degree (6 th and 7 th position out of 148) in the general network. The causal genes of central diseases, with a major impact on multimorbidity, have the potential to influence multiple diseases 52 .…”

Clustering of a Health Dataset Using Diagnosis Co-Occurrences

“…This section describes the proposed Hierarchical Agglomerative Clustering for cooccurrence analysis, denoted HAC-cooc, which clusters the blocks of diagnoses [23,24]. The algorithm constructs a clustering solution by first considering each diagnosis block as a cluster and then by iteratively agglomerating pairs of diagnosis clusters using a local rule based on the maximization of the RR measure.…”

“…The literature on multimorbidity patterns was reviewed in 2014 by Pradros-Torres et al and in 2019 by Busija et al, and several methods and population contexts were identified [16,17]. The methods were factor analysis, to discover underlying common factors of diseases [18,19]; clustering analysis, to regroup either diseases or people into clusters [20][21][22]; or network analysis, to study the links between pairs of diseases [23]. These approaches are mainly focused on disease analysis of complex and chronic patients, which represent the greatest burden on the healthcare economy.…”

Increases in multimorbidity with weight class in the United States

“…Amell et al used network analysis to find age and gender-related trends in disease association. 25 They were also able to indicate diseases with low cumulative risk, which still inform continued disease clustering, and pleiotropy (two or more unrelated effects in the production of a single disease) influencing multimorbidity. Miles et al contend that multimorbidities occur when stressors from an individual's physical and sociocultural environment provoke maladaptive responses in more than one of the body's numerous interconnected physiological networks, thus resulting in interrelated pathologies and chronic conditions.…”

“…28 Despite the strong causal connections between weight increase and comorbidity, most previous studies have only assessed static comorbidity patterns and differences in the prevalence and interactions of comorbidities by age and gender. 23,25 What is already known about this subject?…”