ArticlePloS one2021
Understanding interactions between risk factors, and assessing the utility of the additive and multiplicative models through simulations.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Urbanization, air pollution, and water pollution: Identification of potential environmental risk factors associated with amyotrophic lateral sclerosis using systematic reviews.Frontiers in neurology · 2023Pooled it
- Baseline and longitudinal joint associations of alcohol consumption and obesity with diabetes risk: evaluating multiplicative and additive interactions.Diabetologia · 2026Article
- Phosphate homeostasis and apparent treatment-resistant hypertension in chronic kidney disease: the Chronic Renal Insufficiency Cohort (CRIC) study.Hypertension research : official journal of the Japanese Society of Hypertension · 2026Article
- Risk Factors Associated With the Occurrence and Recurrence of Benign Paroxysmal Positional Vertigo in Koreans: A Nested Case-Control Study.Clinical and experimental otorhinolaryngology · 2025Article
- Impact of multimorbidity and cognitive impairment on depressive symptoms in community-dwelling older adults and their interaction effects.Scientific reports · 2025Article
- Association of pre- and post-diagnosis dietary total antioxidant capacity (TAC) and composite dietary antioxidant index (CDAI) with overall survival in patients with ovarian cancer: a prospective cohort study.Journal of translational medicine · 2025Article
- Dietary B-complex vitamins and sleep quality in relation to cognitive impairment among older adults: non-linear associations and evidence of additive interaction.Frontiers in nutrition · 2025Article
- Assessing hemorrhagic risks in combination therapy: implications of angiogenesis inhibitors and immune checkpoint inhibitors.Frontiers in immunology · 2025Article
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- Have We Been Underestimating Modifiable Dementia Risk? An Alternative Approach for Calculating the Combined Population Attributable Fraction for Modifiable Dementia Risk Factors.American journal of epidemiology · 2023Article
- The societal cost of modifiable risk factors in Singapore.BMC public health · 2023Article
- Genetics of rheumatoid arthritis.Seminars in immunopathology · 2022Review
- Recent advances in sarcoidosis genomics: epigenetics, gene expression, and gene by environment (G × E) interaction studies.Current opinion in pulmonary medicine · 2020Review
- A Gene-Environment Interaction Between Smoking and Gene polymorphisms Provides a High Risk of Two Subgroups of Sarcoidosis.Scientific reports · 2019Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the genetic background of complex diseases requires the expansion of studies beyond univariate associations. Therefore, it is important to use interaction assessments of risk factors in order to discover whether, and how genetic risk variants act together on disease development. The principle of interaction analysis is to explore the magnitude of the combined effect of risk factors on disease causation. In this study, we use simulations to investigate different scenarios of causation to show how the magnitude of the effect of two risk factors interact. We mainly focus on the two most commonly used interaction models, the additive and multiplicative risk scales, since there is often confusion regarding their use and interpretation. Our results show that the combined effect is multiplicative when two risk factors are involved in the same chain of events, an interaction called synergism. Synergism is often described as a deviation from additivity, which is a broader term. Our results also confirm that it is often relevant to estimate additive effect relationships, because they correspond to independent risk factors at low disease prevalence. Importantly, we evaluate the threshold of more than two required risk factors for disease causation, called the multifactorial threshold model. We found a simple mathematical relationship (square root) between the threshold and an additive-to-multiplicative linear effect scale (AMLES), where 0 corresponds to an additive effect and 1 to a multiplicative. We propose AMLES as a metric that could be used to test different effects relationships at the same time, given that it can simultaneously reveal additive, multiplicative and intermediate risk effects relationships. Finally, the utility of our simulation study was demonstrated using real data by analyzing and interpreting gene-gene interaction odds ratios from a rheumatoid arthritis case-control cohort.
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