ArticleFrontiers in public health2025
Comorbidity patterns and implications for disease control: a network analysis of medical records from Shanghai, China.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Multimorbidity patterns among fragility fracture patients aged 50 + years in China and the US.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Article
- Integrative analysis of serum lipids and chronic gastritis: causal insights from mendelian randomization and experimental models.Lipids in health and disease · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The aging problem in Shanghai is rapidly increasing, leading to the development of chronic comorbidities in older adults. Studying the correlations within comorbidity patterns can assist in managing disease prevention and implicate early control. Objectives: This study was a cross-sectional analysis based on a large sample size of 3,779,756 medical records. A network analysis and community classification were performed to illustrate disease networks and the internal relationships within comorbidity patterns among older adults in Shanghai. Methods: The network analysis and community classification were performed using the IsingFit and Fast-greedy community functions. Datasets, including disease codes and disease prevalence, were collected from medical records. Results: The top five prevalent diseases were hypertension (64.78%), chronic ischemic heart disease (39.06%), type 2 diabetes mellitus (24.97%), lipid metabolism disorders (21.79%), and gastritis (19.71%). The sampled population showed susceptibility to 11 comorbidities associated with hypertension, 9 with diabetes, 28 with ischemic heart disease, 26 with gastritis, and 2 with lipid metabolism disorders in male patients. Diseases such as lipid metabolism disorders, gastritis, fatty liver, polyps of the colon, osteoporosis, atherosclerosis, and heart failure exhibited strong centrality. Conclusion: The most common comorbidity patterns were dominated by ischemic heart disease and gastritis, followed by a ternary pattern between hypertension, diabetes, and lipid metabolism disorders. Male patients were more likely to have comorbidities related to cardiovascular and sleep problems, while women were more likely to have comorbidities related to thyroid disease, inflammatory conditions, and hyperuricemia. It was suggested that healthcare professionals focus on monitoring relevant vital signs and mental health according to the specific comorbidity patterns in older adults with chronic diseases, to prevent the development of new or more severe comorbidities.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.