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At least 19 recordsLinked to original sources
PubMed1987

Constructing causal scenarios: a knowledge structure approach to causal reasoning.

A model of causal reasoning based on Schank and Abelson's (1977) analysis of knowledge structures is presented. The first part of this article outlines the necessary characteristics of such a model. It is argued that a central attributional problem is to explain extended sequences of behavior. To do this people must relate actions in a sequence to one another and construct a coherent scenario from them. Because the relation among actions is not given, people must use detailed social and physical knowledge to make connecting inferences. The resulting scenario typically includes information about the plans and goals of the actor. The second part of this article analyzes how the knowledge structures outlined by Schank and Abelson (1977)--scripts, plans, goals, and themes--can be used to construct such causal scenarios, and it presents a process model for the construction of such scenarios. The last part of this article examines the implications of this model and its relations to other attribution models by Kelley (1967, 1971a, 1971b) and Jones (Jones & Davis, 1965; Jones & McGillis, 1976).

Cognition↗
PubMed1989

Constructing a causal model of African human trypanosomiasis. The Antwerp Trypanosomiasis Causal Modelling Group.

Following the TDR/WHO sponsored Workshop on Modelling Sleeping Sickness Epidemiology and Control, 25-29 January 1988, Antwerp, a group of scientists at the Institute of Tropical Medicine "Prince Leopold" (ITM), Antwerp, started to develop a causal model of human african trypanosomiasis. The group hypothesised a series of relations between determinants and associated factors of the prevalence of sleeping sickness. These relations were pictured in a logically structured hierarchial representation of the causal web of sleeping sickness.

Animals↗
PubMed2024

Transcriptome-Wide Root Causal Inference.

Root causal genes correspond to the first gene expression levels perturbed during pathogenesis by genetic or non-genetic factors. Targeting root causal genes has the potential to alleviate disease entirely by eliminating pathology near its onset. No existing algorithm discovers root causal genes from observational data alone. We therefore propose the Transcriptome-Wide Root Causal Inference (TWRCI) algorithm that identifies root causal genes and their causal graph using a combination of genetic variant and unperturbed bulk RNA sequencing data. TWRCI uses a novel competitive regression procedure to annotate cis and trans-genetic variants to the gene expression levels they directly cause. The algorithm simultaneously recovers a causal ordering of the expression levels to pinpoint the underlying causal graph and estimate root causal effects. TWRCI outperforms alternative approaches across a diverse group of metrics by directly targeting root causal genes while accounting for distal relations, linkage disequilibrium, patient heterogeneity and widespread pleiotropy. We demonstrate the algorithm by uncovering the root causal mechanisms of two complex diseases, which we confirm by replication using independent genome-wide summary statistics.

Journal Article↗
PubMed2026

Causal associations between hormone replacement therapy and brain structure: Evidence from large-scale Mendelian randomization and double machine learning.

BACKGROUND: Hormone replacement therapy (HRT) is widely prescribed for the management of hormone deficiency, particularly during menopause, yet its causal effects on human brain structure remain incompletely understood. Observational studies have reported heterogeneous associations, underscoring the need for robust causal inference. METHODS: We applied an integrated causal framework combining two-sample Mendelian Randomization (MR) and Double Machine Learning (DML) to evaluate the effects of four HRT-related exposures-age at initiation, age at cessation, ever-use of HRT, and a composite medication-based phenotype-on 1366 brain imaging-derived phenotypes from the UK Biobank. Genetic instruments were derived from large-scale GWAS summary statistics, and causal estimates were validated using non-parametric DML models with cross-fitting and performance evaluation. RESULTS: Genetic instruments for age at HRT initiation, age at cessation, and ever-use of HRT were strong (median F-statistics 16.29-36.66). MR analyses identified a causal association between later initiation of HRT and lower orientation dispersion in the right inferior cerebellar peduncle (ubm-a-542; primary finding, no pleiotropy detected). An additional association with the left tapetum FA (ubm-a-243) was identified but exhibited significant directional horizontal pleiotropy (MR-Egger intercept P = 0.001) and is excluded from primary conclusions (Supplementary Note S2). Later cessation of HRT was associated with increased cortical thickness in the left middle occipital gyrus, reduced surface area in the left frontopolar cortex, and increased orientation dispersion in the splenium of the corpus callosum. Ever-use of HRT was causally linked to larger volumes of the right inferior frontal gyrus and right nucleus accumbens. These associations were corroborated by independent DML validation, which provided causally debiased estimates robust to high-dimensional confounding. Results for ukb-b-8080 (median F = 1.45) are provided in Supplementary Note S1 only; weak-instrument bias precludes causal inference. CONCLUSIONS: This study provides genetic-instrument-based and machine-learning-validated evidence for causal associations between HRT exposure-particularly its timing and lifetime use-and specific features of human brain structure, including white-matter microarchitecture, cortical thickness, and regional brain volume. These findings are FDR-controlled within exposures and independently replicated by DML, but require replication in external neuroimaging GWAS cohorts to establish definitive causal conclusions. They highlight the neurobiological relevance of sex steroid exposure and inform future research on brain aging and personalized hormone-based interventions.

Humans↗
PubMed2025

Transcriptome-wide root causal inference.

Root causal genes correspond to the first gene expression levels perturbed during pathogenesis by genetic or non-genetic factors. Targeting root causal genes has the potential to alleviate disease entirely by eliminating pathology near its onset. No existing algorithm has been designed to discover root causal genes from observational data alone. We therefore propose the Transcriptome-Wide Root Causal Inference (TWRCI) algorithm that identifies root causal genes and their causal graph using a combination of genetic variant and unperturbed bulk RNA sequencing data. TWRCI uses a novel competitive regression procedure to annotate cis and trans-genetic variants to the gene expression levels they directly cause. The algorithm simultaneously determines the sequence in which gene expression changes propagate through the system to pinpoint the underlying causal graph and estimate root causal effects. TWRCI outperforms alternative approaches across a diverse group of metrics by directly targeting root causal genes while accounting for distal relations, linkage disequilibrium, patient heterogeneity and widespread pleiotropy. We demonstrate the algorithm by uncovering the root causal mechanisms of two complex diseases, which we confirm by replication using independent genome-wide summary statistics.

Algorithms↗
PubMed2024

Causal effects of sedentary behaviours on the risk of migraine: A univariable and multivariable Mendelian randomization study.

BACKGROUND: Migraine is a common and burdensome neurological disorder. The causal relationship between sedentary behaviours (SBs) and migraine remains instinct. We aimed to evaluate the roles of SBs including watching TV, using computer and driving in the risk of migraine. METHODS: We conducted a univariable and multivariable Mendelian randomization (MR) study based on summary datasets of large genome-wide association studies. The inverse variance weighted method was utilized as the primary analytical tool. Cochran's Q, MR-Egger intercept test, MR pleiotropy residual sum and outlier and leave-one-out were conducted as sensitivity analysis. Additionally, we performed a meta-analysis to combine the causal estimates. RESULTS: In the discovery analysis, we identified causal associations between time spent watching TV and an increased risk of migraine (p&#x2009;=&#x2009;0.015) and migraine without aura (MO) (p&#x2009;=&#x2009;0.002). Such causalities with increasing risk of migraine (p&#x2009;=&#x2009;0.005), and MO (p&#x2009;=&#x2009;0.006) were further verified using summary datasets from another study in the replication analysis. There was no significant causal association found between time spent using computer, driving and migraine or its two subtypes. The meta-analysis and multivariable MR analysis also strongly supported the causal relationships between time spent watching TV and an increased risk of migraine (p&#x2009;=&#x2009;0.0003 and p&#x2009;=&#x2009;0.034), as well as MO (p&#x2009;<&#x2009;0.0001 and p&#x2009;=&#x2009;0.0004), respectively. These findings were robust under all sensitivity analysis. CONCLUSIONS: Our study suggested that time spent watching TV may be causally associated with an increased risk of migraine, particularly MO. Large-scale and well-designed cohort studies may be warranted for further validation. SIGNIFICANCE STATEMENT: This study represents the first attempt to investigate whether a causal relationship exists between SBs and migraine. Utilizing MR analysis helps mitigate reverse causation bias and confounding factors commonly encountered in observational cohorts, thereby enhancing the robustness of derived causal associations. Our MR analysis revealed that time spent watching TV may serve as a potential risk factor for migraine, particularly MO.

Humans↗
PubMed2026

NLCD: A method to discover nonlinear causal relations among genes.

Distinguishing correlation from causation is a fundamental challenge in many scientific fields, including biology, especially when interventions like randomized controlled trials are infeasible and only observational data are available. Methods based on statistical tests of conditional independence within the Mendelian Randomization framework can detect causality between two observed variables that are each associated with a third instrumental variable. However, these methods for detecting causal relationships between traits (e.g., two gene expression or clinical traits associated with a genetic variant, all observed in the same population) often assume a linear relationship, thereby hindering the discovery of causal gene networks from genomics data. We have developed NLCD, a method for NonLinear Causal Discovery from genomics data based on nonlinear regression modeling and conditional feature importance scoring. NLCD uses these techniques to extend the statistical tests in an existing linear causal discovery method called the Causal Inference Test (CIT). We benchmarked NLCD against current state-of-the-art methods: CIT, Findr, and MRPC. On simulated datasets, NLCD performs comparably to most methods in detecting linear relations (Average AUPRC (Area Under the Precision-Recall Curve) of NLCD&#x2009;=&#x2009;0.94, CIT&#x2009;=&#x2009;0.94, Findr&#x2009;=&#x2009;0.94, and MRPC&#x2009;=&#x2009;0.99), and outperforms them in detecting nonlinear (sine and sawtooth type) relations between two genes (Average AUPRC of NLCD&#x2009;=&#x2009;0.76, CIT&#x2009;=&#x2009;0.60, Findr&#x2009;=&#x2009;0.56, and MRPC&#x2009;=&#x2009;0.73). When tested on a nonlinear subset of a yeast genomic dataset to recover known causal relations involving transcription factors, NLCD and CIT performed comparable to each other and slightly better than Findr and MRPC (Average AUPRC of NLCD&#x2009;=&#x2009;0.82, CIT&#x2009;=&#x2009;0.81, Findr&#x2009;=&#x2009;0.71, and MRPC&#x2009;=&#x2009;0.54). On application to a human genomic dataset, NLCD revealed active causal gene pairs (IRF1 &#x2192; PSME1 and HLA-C &#x2192; HLA-T) in the muscle tissue, and clarified the promises and challenges in discovering causal gene networks in tissues under in vivo human settings.

Humans↗
PubMed2025

Causal relationship between albumin, total protein, and colorectal cancer risk: A 2-sample Mendelian randomization study.

Albumin (ALB) and total protein (TP) are vital constituents of the blood, and their levels and roles in the risk of colorectal cancer (CRC) are of significance. Previous observational studies have reported correlations among ALB, TP, and CRC. However, the existence of a causal relationship between ALB and CRC in European populations has not been adequately investigated and the causal link between TP and CRC remains unexplored. To address these gaps, we applied Mendelian randomization (MR) to investigate the potential causal relationship between ALB, TP, and CRC. Two-sample MR analysis was used to investigate whether there was a causal relationship between ALB, TP, and CRC. Our exposure data were extracted from genome-wide association study (GWAS) databases sourced from the UK Biobank, containing 315,268 and 314,921 Europeans participants for ALB and TP analyses, respectively. Single nucleotide polymorphisms that were significantly associated with ALB and TP were assessed using GWAS datasets. Our data were derived from the FinnGen Consortium CRC GWAS, which contained 6509 CRC cases and 28,7137 controls. Causal inference between ALB, TP, and CRC was performed using 3 MR methods: inverse variance weighting (IVW), MR-Egger, and weighted median. The IVW analysis showed no significant causal association between ALB and CRC (OR&#x2005;=&#x2005;1.04, 95% CI&#x2005;=&#x2005;0.89-1.21, P&#x2005;=&#x2005;.65). In contrast, the IVW analysis for TP and CRC showed a significant causal association (OR&#x2005;=&#x2005;0.78, 95% CI&#x2005;=&#x2005;0.66-0.92, P&#x2005;=&#x2005;.003), suggesting a reduced risk of CRC. Through a 2-sample MR study investigating the causal relationship between ALB, TP, and CRC in a European population, our findings revealed a significant causal relationship between TP and a reduced risk of CRC.

Humans↗
PubMed1992

Causality in medicine: towards a theory and terminology.

One of the cornerstones of modern medicine is the search for what causes diseases to develop. A conception of multifactorial disease causes has emerged over the years. Theories of disease causation, however, have not quite been developed in accordance with this view. It is the purpose of this paper to provide a fundamental explication of aspects of causation relevant for discussing causes of disease. The first part of the analysis will discuss discrimination between singular and general causality. Singular causality, as in the specific patient, is a relation between a concrete sequence of causally linked events. General causation, e.g. as in disease etiology, means various categories of causal relations between event types. The paper introduces the concept of a reference case serving as a source for causal inference, reaching beyond the concept of general causality. The second part of the analysis provides exemplification of a theory of causation suitable for discussing singular causation. The chain of events that induce a disease state can be identified as effective causal complexes, each complex composed of non-redundant components, which separately contribute to the effect of the complex, without the individual component being necessary or sufficient in itself to produce the effect. In the third part of the analysis the theory is elaborated further. Causes, defined as non-redundant components, can furthermore be differentiated according to their avoidability, according to theories about human error or by the potential of eradication. Multifactorial models of disease creates a need for systematic approaches to causal factors. The paper proposes a taxonomical terminology that serves this purpose.

Adult↗
PubMed2026

Causal Relationships Between Oral Microbiota and Inflammatory Skin Diseases.

INTRODUCTION AND AIMS: The oral microbiome has been increasingly linked to systemic inflammation and immune dysregulation, but whether specific oral bacteria causally contribute to inflammatory skin diseases remains unclear due to confounding and reverse causation. This study aimed to assess the causal effects of 43 oral microbiota taxa on the risk of five inflammatory skin diseases using a Mendelian randomization (MR) approach. METHODS: We performed a two-sample MR analysis using genetic instruments for oral microbiota derived from publicly available genome-wide association studies and outcome data from the FinnGen consortium. Causal effects of oral taxa on systemic lupus erythematosus, vitiligo, pemphigus, localized scleroderma, and dermatitis herpetiformis were estimated. The inverse-variance weighted method served as the primary analysis, complemented by sensitivity analyses to evaluate horizontal pleiotropy, heterogeneity, and reverse causality. RESULTS: MR analyses identified several putative causal associations between oral microbiota and inflammatory skin diseases. Genus Granulicatella and an unknown Streptococcus species (ASV0009) showed causal effects on systemic lupus erythematosus. Family Lachnospiraceae_[XIV] and an unknown Rothia species (ASV0016) were associated with vitiligo. Five oral microbiota taxa demonstrated causal associations with pemphigus. Actinomyces species micronuciformis was linked to localized scleroderma. Order Fusobacteriales and an unknown Neisseria species (ASV0004) were associated with dermatitis herpetiformis. No significant heterogeneity or horizontal pleiotropy was detected in sensitivity analyses. CONCLUSION: This MR study provides genetic evidence supporting a causal role of specific oral bacteria in the development of several inflammatory skin diseases, highlighting the oral microbiome as a potential contributor to cutaneous autoimmunity and inflammation. CLINICAL RELEVANCE: Our findings highlight the putative role of the oral microbiome as a plausible candidate for mechanistic and clinical investigations into the prevention or adjunctive management of selected inflammatory skin diseases. However, oral hygiene improvement, targeted antimicrobials, and other microbiota-directed interventions were not directly tested in this MR study and remain hypothetical strategies requiring validation in experimental and clinical studies.

Humans↗
PubMed1975

The chemotherapy of rodent malaria, XXIII Causal prophylaxis, part II: Practical experience with Plasmodium yoelii nigeriensis in drug screening.

Data are presented on the causal prophylactic action of about 100 compounds of various types against Plasmodium yoelii nigeriensis N67 in mice. Examples are given to show how action against pre-erythrocytic schizonts may be differentiated from action on emerging erythrocytic stages. In a series of 35 8-aminoquinolines, all but 10 showed definite causal prophylactic activity at tolerated doses. The data permit the compounds to be ranked in order of activity, and many are shown to be more active in this test system than primaquine. Marked causal prophylactic activity is displayed by a variety of quinone structures, several of which show a significant residual action on blood stages. A high level of activity is found in dihydrofolate reductase inhibitors within several chemical classes. Rorguanil is more effective as a causal prophylactic than a blood schizontocide in the mouse as in man. Sulphonamides and sulphones are also effective in this system. The active levels are influenced by the content of PABA in the diet of the hosts. Causal prophylactic action has been detected in a number of experimental compounds including some antibiotics (such as tetracycline and clindamycin). The pyrocatechol RC 12 shows only slight activity at the maximum tolerated dose. Chloroquine, mepacrine, quinine, quinolinemethanols and phenanthrenemethanols are inactive as causal prophylactics. It is concluded that a rodent malaria-mouse model does provide a relatively simple model for the screening of drugs for causal prophylaxis, and the data so obtained are of relevance to the detection of causal prophylactics against human malaria.

Amidines↗
PubMed1992

Causal propositions in clinical research and practice.

The concept of causation is central to clinical research and practice. The health science literature on causality, largely contributed by epidemiologists, has examined the population-based question of whether an exposure can cause a given health outcome. Most of this literature has focused on criteria for assessing causality, rather than attempting to define it. Moreover, the population-based approach is rather distant from the individual persons in whom causes must act, which has led to different perspectives on causality among epidemiologists and health policy markers, on the one hand, and clinical practitioners and the lay public, on the other. We attempt to bridge the gap between these perspectives by defining three probabilistic causal propositions based on the locus (individual vs population) and time frame (past vs future outcome) to which they refer, beginning with the individual in whom a health outcome has already occurred ("retrodictive" causal propositions, i.e. It Did) and proceeding to "potential" causal propositions (It Can) for populations and "predictive" causal propositions (It Will) for individuals or populations. We conclude by showing how attention to these distinctions may help avoid common pitfalls that can impair clinical or public health decision-making.

Causality↗
PubMed1990

Epigenesis theory: a mathematical model relating causal concepts of pathogenesis in individuals to disease patterns in populations.

A mathematical modeling approach called epigenesis theory is presented which relates three aspects of pathogenesis to the population distribution of disease. The three aspects of pathogenesis involve how two or more measured variables interact. They are 1) whether the measured variables are related to the same causal action, 2) whether there is only one pathogenic process leading to disease, and 3) whether the measured variables contribute to the same pathogenic process. Epigenesis theory defines the following multivariable relations between two disease causes: 1) "Complementary" causes contribute different causal actions to the sole pathogenic process leading to disease. They have multiplicative relations. 2) "Separate process" causes contribute different causal actions to different pathogenic processes. They have the relations of simple independent action which are slightly less than additive. 3) "Intermediate" causes contribute different causal actions to the same pathogenic process in the presence of additional pathogenic processes where at most one of them may also participate. They have relations somewhere between multiplicative and simple independent actions. 4) "Cooperative-competitive" causes share the same causal action and act within the same pathogenic process. Their relations can change from greater than multiplicative to less than simple independent action at increasing dichotomization points of the measured variables. Epigenesis theory unifies the sufficient-component causes model and the simple independent action model and exceeds either model in the range of observations it can explain. It is most useful given directly causal measured variables and specific disease outcomes, but it will assist in etiologic investigations of nonspecific outcomes in which new disease classifications are proposed. While it is less useful given surveillance-type variables such as age or sex or outcomes resulting from numerous pathogenic processes such as death, it gains utility as more causal variables are entered into an analysis and as more cut points of continuous complementary, independent, or intermediate variables are distinguished.

Adult↗
PubMed2026

Causal relationships between somatic movement, brain structures, and mental well-being: A multi-stage Mendelian randomization study.

BACKGROUND: While the relationships between somatic movement, mental well-being, and brain health have been well established, the causal nature and underlying mechanisms of such associations remain incompletely understood. METHODS: By applying multi-stage Mendelian randomization to multi-source summary data derived from genome-wide association studies, we examined the causal effects of 4 somatic movement measures on 2 mental well-being indices and 13 types of brain structures, followed by testing the mediating roles of brain structures in accounting for the causal associations between somatic movement and mental well-being. RESULTS: Two-sample Mendelian randomization revealed that more physical activity was causally associated with greater mental well-being (life satisfaction and positive affect), while more sedentary behavior (longer leisure screen time and more sedentary behavior at work) with lower mental well-being. With respect to brain structures, sedentary behavior was causally linked to decreased volume, surface area, and local gyrification index in distributed cortical regions. Remarkably, decreased surface area of the piriform cortex was found to mediate the causal associations between sedentary behavior and lower mental well-being. CONCLUSIONS: Our findings not only complement and extend earlier reports on the associations of somatic movement with mental well-being and brain health by further resolving the causality but also help elucidate the neural mechanisms by which sedentary behavior adversely affects mental well-being.

Humans↗
PubMed2026

Identification and genetic validation of potential therapeutic targets for pulmonary hypertension through multi-omics causal inference.

Pulmonary hypertension (PH) underscores the urgent need for novel therapeutic targets. This study aimed to employ a proteome-wide Mendelian randomization (MR) approach to systematically identify circulating proteins causally associated with PH, thereby providing genetically validated candidate targets for drug development. We adopted a 2-sample MR design, integrating large-scale plasma proteomic quantitative trait loci (pQTL) data (encompassing 4148 proteins) and summary statistics from a large-scale PH genome-wide association study (2047 cases, 8301 controls). Candidate targets were screened through a multilayered analytical pipeline comprising proteomic MR, transcriptomic MR, and summary-data-based Mendelian randomization. The ultimately identified MR-Identified Causal Candidate Targets (MR-ICTs) underwent rigorous Bayesian colocalization analysis, followed by biological characterization through functional enrichment analysis, single-cell transcriptomics, and phenome-wide association studies. Through robust genetic causal inference, this study provides that circulating proteins such as LYZ, GREM2, NID1, and PF4V1 play causal roles in PH pathogenesis. These findings offer a set of rigorously genetically validated, high-priority therapeutic targets for developing novel PH treatments, specifically addressing key pathological mechanisms such as innate immunity, BMP signaling pathway dysregulation, and platelet activation. Our multi-dimensional analysis ultimately identified 6 MR-ICTs causally associated with PH. Notably, the causal associations for lysozyme C (LYZ), gremlin-2 (GREM2), nidogen-1 (NID1), and platelet factor 4 variant 1 (PF4V1) were stringently validated by Bayesian colocalization analysis (posterior probability for hypothesis 4 [PPH4], indicating a shared causal variant, > 0.99). Functional enrichment analysis revealed significant involvement of these targets in immune response and TGF-&#x3b2; signaling pathways. Single-cell analysis further elucidated their cell-type-specific expression, with LYZ predominantly expressed in monocytes and PF4V1 almost exclusively in platelets.

Hypertension, Pulmonary↗
PubMed2026

The causal relationships and potential pathways between birth weight and cardiovascular diseases: A human genomics study.

The causal relationships and potential pathways between birth weight (BW) and various cardiovascular diseases (CVDs) remain unclear, particularly when discriminating maternal and fetal contributions of BW to CVDs. Leveraging the genome-wide association studies (GWASs) of BW (N&#x2005;=&#x2005;321,223) and a range of CVDs (ncases&#x2005;=&#x2005;43,676-181,522), we performed a 2-sample Mendelian randomization (MR) analysis to estimate the causal effect of BW, fetal-specific BW, and maternal-specific BW on coronary artery disease (CAD), myocardial infarction (MI), heart failure (HF), atrial fibrillation (AF), and stroke. Furthermore, we applied a stepwise MR analysis approach to assess the potential involvement of childhood body mass index (CBMI) and age at menarche (AAM) in the causal pathways from BW to CVDs, while considering adult BMI. Finally, we performed colocalization analyses to justify the different biological mechanisms of maternal-specific and fetal-specific BW. The 2-sample MR analysis revealed that genetically predicted higher BW per standard deviation (SD) was associated with a decreased risk of CAD (odds ratio [OR]&#x2005;=&#x2005;0.804, 95% confidence interval [CI]: 0.731-0.883), MI (OR&#x2005;=&#x2005;0.720, 95% CI: 0.638-0.814), and stroke (OR&#x2005;=&#x2005;0.900, 95% CI: 0.823-0.985), but an increased risk of AF (OR&#x2005;=&#x2005;1.279, 95% CI: 1.160-1.410). Similar associations were observed for fetal-specific/maternal-specific BW. The stepwise MR analysis indicated that CBMI and AAM could serve as factors linking BW/fetal-specific BW and CVDs, albeit in different roles, by displaying an indirect causal effect through adult BMI. However, for maternal-specific BW, our results failed to support a causal effect on CBMI or AAM. Colocalization analyses supported the distinct biological mechanisms for maternal-specific and fetal-specific BW by showing different causal genes. The study suggested that both fetal genotype and intrauterine environmental exposure contribute to the causal associations. Additionally, AAM and CBMI may play a role in the pathways linking BW and CVDs, though the effect was only observed for fetal-specific BW.

Humans↗