Adèle Helena Ribeiro ¹ Milena Crnkovic ^2* Jaqueline Lopes Pereira ³ Regina Fisberg ³ Flavia Mori Sarti ⁴ MARCELO ROGERO ³ Dominik Heider ^1* Andressa Cerqueira ²

• ¹ Institute of Medical Informatics, Faculty of Medicine, University of Münster, Münster, North Rhine-Westphalia, Germany
• ² Department of Statistics, Center for Exact Sciences and Technology, Federal University of São Carlos, São Carlos, São Paulo, Brazil
• ³ Department of Nutrition, Faculty of Public Health, University of São Paulo, São Paulo, São Paulo, Brazil
• ⁴ Schoool of Arts, Sciences and Humanities, University of Sao Paulo, São Paulo, São Paulo, Brazil

Cardiometabolic diseases, a leading global health concern, arise from a complex interplay of lifestyle choices, genetic predispositions, and biochemical markers. Although extensive research has uncovered strong associations among various risk factors and these diseases, grasping their causal relationships is vital for gaining deeper mechanistic insights and designing effective prevention and intervention strategies. We address this gap by introducing anchorFCI, a novel adaptation of the conservative Really Fast Causal Inference (RFCI) algorithm designed to enhance the discovery of causal relationships by strategically selecting and integrating reliable anchor variables from an additional set known not to be caused by the variables of interest. This approach is particularly well-suited for learning causal networks involving phenotypic, clinical, and sociodemographic factors, leveraging genetic variables recognized as not being influenced by these factors. By integrating these anchor variables along with knowledge of their non-ancestral relationships, anchorFCI effectively handles latent confounding while enhancing both robustness and discovery power. We demonstrate its effectiveness using from the 2015 ISA-Nutrition study in S ão Paulo, Brazil, and further estimate the effect sizes of the uncovered causal relationships with state-of-the-art tools from Judea Pearl's framework, presenting a fully data-driven causal inference pipeline. The results not only support many established causal relationships but also elucidate their interconnections within a complex network, enhancing our understanding of the broader dynamics and the multifaceted nature of cardiometabolic risk.