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PubMed2026

Association between 1400 blood metabolites and the risk of ankylosing spondylitis: A 2-stage, 2-sample Mendelian randomization study.

Human blood metabolites have been closely linked to ankylosing spondylitis (AS) in observational studies, yet direct causal evidence remains limited. This study aims to use Mendelian randomization (MR) to pinpoint causal metabolites associated with AS and to predict potential side effects of metabolite interventions. Genetic instruments for exposure were sourced from a genome-wide association study of 1400 blood metabolites, while genome-wide association study data for AS outcomes were derived from the FinnGen cohort. The primary MR analysis was conducted using the inverse variance weighted method. Supplemental analyses were conducted using weighted median, MR-Egger, simple mode, and weighted mode methods, while sensitivity analyses were performed to evaluate heterogeneity and pleiotropy. A replication analysis using an additional the UK Biobank cohort was also performed to determine metabolites associated with AS. The Steiger test and linkage disequilibrium score regression were used to further strengthen causal inference. Lastly, a phenome-wide Mendelian randomization analysis was performed to investigate the potential on-target side effects of metabolite interventions. After comprehensive analyses, 3 metabolites (the 2'-deoxyuridine levels, the hate to mannose ratio, and the Uridine to 2'-deoxyuridine ratio) were identified as being genetically associated with AS. The phenome-wide Mendelian randomization analysis revealed that the hate to mannose ratio might have deleterious effects on 4 other diseases, while no significant associations were found for the 2'-deoxyuridine levels or the uridine to 2'-deoxyuridine ratio with other diseases. This systematic MR analysis unveiled the potential role of the 2'-deoxyuridine levels, hate to mannose ratio and uridine to 2'-deoxyuridine ratio as the causal mediator in the development of AS. Considering the advantages and disadvantages, 2'-deoxyuridine appears as the most promising prospective therapeutic target for the prevention of AS.

Humans↗
PubMedDate not supplied

[Causality in urologic research].

Clinical-epidemiological research may orient us about the causes of disease, the relationships among them, and the relative magnitudes of their effects. The objective of this article is to link the notion of cause with the basic clinical-epidemiological parameters. There are different models explaining causality. All of them present the possible etiologic explanations for the diseases, taking into consideration the current knowledge at the time they have been posed. We start from a purely determinist conception, understanding causality as a constant connection between two factors x and y, unique, and perfectly predictable. Currently, this model is inadequate to be applied to many diseases. Many researchers have modified the determinist model to explain the multiple causality of disease, posing the existence of associations of causal factors, more than single factors, being these associations treated as sufficient cause (i.e. as a group of minimal conditions and events that inevitably produce the disease). That determinist concept of causality is supplemented with the probabilistic concept. The theory of probability is used in it, as well as the related statistical, methods, to empirically evaluate a possible association that is believed causal. As a consequence of the lack of certainty of the prediction at the individual level, the theoretical notion of cause is replaced by the empirical concept of risk factor, referring to a variable which is considered to be related to the probability that one individual develops the disease. Causal inference in epidemiology is the logic development of a theory, based on observations and arguments that attribute the presence (association) of a disease to one or more risk factors. We will follow the principles posed by B. Hill for the complex process called scientific generalization. To correctly perform this relationship between our ideas and are observations it is absolutely important to start from a correct election of the study design with which the research is undertaken.

Biomedical Research↗
PubMed2026

Large-Scale Proteomic Profiling of Incident Heart Failure and Its Subtypes in Older Adults.

BACKGROUND: Heart failure (HF) and its main subtypes, heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF), impose an enormous health burden on elders. Assessment of the circulating proteome to illuminate pathogenesis could open new opportunities for treatment. METHODS: We conducted a plasma proteomics screen of incident HF and its subtypes in 2 older population-based cohorts, the CHS (Cardiovascular Health Study) and the AGES-RS (Aging, Gene/Environment Susceptibility-Reykjavik Study). The 2 studies used SomaLogic platforms, with 4404 aptamers in common. Multivariable Cox models were fit to evaluate individual-protein associations with HF, HFpEF, and HFrEF separately in each cohort, and study-specific associations were combined by fixed-effects meta-analysis. Replication was performed in the ARIC (Atherosclerosis Risk in Communities) cohort. Two-sample Mendelian randomization of HF and its subtypes, along with colocalization analysis, was performed to support causal inference. RESULTS: Among 8599 participants, 1590 experienced incident HF (536 HFpEF, 471 HFrEF). There were 119 proteins associated with HF, 15 proteins with HFpEF, and 11 proteins with HFrEF, at Bonferroni-corrected significance. Among these, 9 have never previously been identified for cardiovascular diseases, and another 61 represent new associations with incident HF or its subtypes. Of these 70 proteins, 55 of the 66 available replicated externally. Mendelian randomization analysis revealed 7 proteins genetically associated with HF at nominal significance; 2 were separately associated with HFpEF, and another 2 with HFrEF. Seven of these 9 proteins (NPDC1 [neural proliferation differentiation and control protein 1], APOF [apolipoprotein F], LMAN2 [lectin, mannose-binding 2], ADIPOQ [adiponectin], CD14 [cluster of differentiation 14], ARHGAP1 [Rho GTPase-activating protein 1], C9 [complement 9]) showed new, possibly causal associations, although we did not detect evidence for colocalization. CONCLUSIONS: In this large-scale proteomic study involving 3 longitudinal cohorts of older adults, we identified and replicated 55 novel protein markers of HF or its subtypes, and 7 new, possibly causal proteins. These proteins may enhance risk prediction, improve understanding of pathobiology, and help prioritize targets for therapeutic development of these foremost disorders in elders.

Humans↗
PubMed1999

Causal criteria in nutritional epidemiology.

Making nutrition recommendations involves complex judgments about the balance between benefits and risks associated with a nutrient or food. Causal criteria are central features of such judgments but are not sufficient. Other scientific considerations include study designs, statistical tests, bias, confounding, and measurement issues. At a minimum, the set of criteria includes consistency, strength of association, dose response, plausibility, and temporality. The current practice, methods, and theory of causal inference permit flexibility in the choice of criteria, their relative priority, and the rules of inference assigned to them. Our approach is as follows. Consistency across study designs is compelling when the studies are of high quality and are not subject to biases. A statistically significant risk estimate with a > 20% increase or decrease in risk is considered a positive finding. A statistically significant linear or otherwise regularly increasing trend reinforces the judgment in favor of a recommendation. A plausible hypothesis likewise reinforces a recommendation, although the rules of inference for biological evidence are highly variable and depend on the situation. Temporality is, for nutrition recommendations, more a consideration of the extent to which a dietary factor affects disease onset or progression. Evidence supporting these criteria provides a strong basis for making a nutrition recommendation, given due consideration of the balance between presumed benefits and presumed harms. Recommendations should make clear their breadth of application; a narrow recommendation involves a single disease or condition whereas a broad recommendation involves all relevant diseases or conditions.

Causality↗
PubMed2026

Shared Genetic Basis, Biological Function and Causal Relationship Between Sleep Traits and Hypothyroidism: Evidence from a Comprehensive Genetic Analysis.

BACKGROUND: This research attempts to clarify whether there are any genetic similarities between sleep traits and hypothyroidism based on publicly accessible large-scale genomewide association studies. METHODS: The methodology included colocalization analysis, cross-phenotype association analysis, and linkage disequilibrium score regression analysis to find common genetic overlap. Through tissue function specificity and functional mapping, we were able to identify the shared genetic level. Genetic instrumental factors were used for causal inference in two-sample univariate and multivariable Mendelian randomization analyses. RESULTS: A hereditary correlation between hypothyroidism and napping during the day and getting up in the morning (rg= -0.0982, P= 0.0007; rg= -0.101, P= 0.0001). MAGI3, and HLA-DRB1 BX296568.1 may be potential targets for shared treatments. Colocalization and tissue-specific analysis demonstrated that the common genes and SNPs were identified in the thyroid, lung, brain, and lymphatic tissues. Functional analysis emphasized the importance of these common genes in processes like as protein transport, inflammatory response, and MHC class II protein synthesis. Furthermore, an association has been established between hypothyroidism and sleep duration (IVW, OR 1.5208; 95% CI 1.1142-2.0758, P=0.0082) and getting up in the morning (IVW, OR 1.8375; 95%CI: 1.4502-2.3284, P=4.73E-07). Furthermore, the reverse MR analysis revealed no causal connection between aberrant sleep traits and hypothyroidism. The enduring impact of insomnia on hypothyroidism persists despite controlling for alcohol consumption and smoking habits. CONCLUSION: Certain genetic correlations between sleep traits and hypothyroidism have been emphasized. These findings may elucidate the origin of comorbidity and have implications for future clinical trials.

Humans↗
PubMed1989

Causal reasoning and inference making in judging the importance of story statements.

The present study investigated the development of the ability to judge the importance of story statements on the basis of their causal properties. Key statements were varied with respect to 2 factors: in terms of the number of their causal relations, and in terms of the kinds of relations they had. Relations were either intraepisodic, that is, connecting statements in the same episode, or interepisodic, that is, connecting statements in different episodes. Children 8, 11, 14, and 18 years of age judged the importance of the statements. Children in all 4 age groups judged statements with many intraepisodic causal relations as more important than statements with few such relations. Only children 11 years and older judged statements as more important when they had interepisodic relations than when they did not. Thus, although young children may be sensitive to quantitative aspects of a statement's relational role within an episode, they may not be as aware of qualitative, that is, structural, differences between kinds of relations. Answers to why questions confirmed these patterns. Older children more often gave answers that crossed episodic boundaries than did the younger children. These findings may reflect age-related differences in children's ability to infer relations between statements and to integrate the information contained in stories. They also attest to the central role that causal inferences play in the interpretation of what is important information in stories.

Adolescent↗
PubMed2003

General linear contrasts on latent variable means: structural equation hypothesis tests for multivariate clinical trials.

Structural equation models articulate the assumed measurement and causal relations among variables, imposing discipline on otherwise unstructured and redundant associations that arise in correlational studies. Biomedical research has eschewed such methods, relying on the generally superior causal inference afforded by randomized controlled trials. Increasingly, however, clinical trials incorporate numerous covariates that are measured but unmanipulated. Most clinical trials now also include multiple correlated endpoints, which can generate ambiguous outcome patterns refractory to simple statistical analysis and interpretation. Modern clinical trials are really multivariate longitudinal studies with at best a component of randomized control; as such, structural equation approaches can add rigour and clarity. The analysis of latent variance (LANOVA) conception combines structural equation and experimental analysis of variance legacies. It allows, for any design that can be decomposed into between-group and within-person models, tests on latent means (that is, the means of the unobserved factors) that are directly analogous to their analysis of variance counterparts. LANOVA variables are either outcomes (which may have a time structure), varying covariates (which may have a time structure), or background covariates (which are static). Allowable causal relations are set-recursive: background covariates can affect all other variables; varying covariates can affect current outcomes and later outcomes and covariates; outcomes can affect only later outcomes. All standard ANOVA and ANCOVA hypotheses can then be tested by proper restrictions among the means and intercepts of these latent variables. Structural equation modelling programs can be used to estimate the models and test the hypotheses. I demonstrate the approach by expressing and testing hypotheses appropriate for two clinical studies that evaluate patient-reported outcomes in populations with solid and haematological malignancies.

Analysis of Variance↗
PubMed2004

Polyarteritis nodosa reports to the vaccine adverse event reporting system (VAERS): implications for assessment of suspected vaccine-provoked vasculitis.

OBJECTIVE: To examine polyarteritis nodosa (PAN) reports to the Vaccine Adverse Event Reporting System (VAERS) as the initial stage in investigating the hypothesis that vaccination can very rarely cause PAN. METHODS: We reviewed PAN reports submitted from 1990 through 2001 using a causal inference framework to evaluate the consistency of the reports' clinical details with this hypothesis. We also reviewed published literature relating to the hypothesized association's biological plausibility. RESULTS: VAERS received 25 PAN reports. Ten met our case definition for definite or possible PAN and had no alternative etiology for PAN identified. Nine of these 10 followed hepatitis B vaccine with a modal peak (4 definite cases) in time to symptom onset 2 weeks after vaccination. However, all potential triggering infections were not excluded, and identification of vaccine antigens in clinical specimens was not attempted. Also, 14 of 25 reports were European, with 11 from France. All 9 French reports with a known diagnosis date began during 1994-97, when autoimmune and rheumatologic events following hepatitis B vaccine were a focus of public concern in France. CONCLUSION: While we identified some supportive evidence, overall, current adverse event reports do not support a causal link between vaccination and PAN. Appropriate prospective evaluation of future post-vaccination PAN cases could add to current knowledge with rigorous confirmation of diagnosis, appropriate testing for possible triggering infections including polymerase chain reaction testing for latent hepatitis B infection, and an attempt to link the vaccine antigen to pathology such as by immunohistochemical staining or immune complex identification.

Adolescent↗
PubMed1992

[Occupational dust exposure and chronic obstructive bronchopulmonary disease. Etiopathogenic approach to the problem of compensation in the mining environment].

In order to support the hypothesis of a causal relationship between exposure to respirable coal mine dusts and chronic obstructive pulmonary disease (COPD), investigations were performed to assess the biological disorders affecting the distal air spaces in coal workers with pneumoconiosis. Broncho alveolar lavage studies demonstrated a continual influx of mononuclear phagocytes loaded with mineral particles, associated with neutrophils in small number, in smokers as well as in non smokers. The alveolar macrophages were in activated state, spontaneously releasing various mediators, particularly interleukin I, Tumor necrosis factor alpha and superoxide anion. So, they appeared as responsible both for a chronic inflammation of the distal airways and for a protease-antiprotease imbalance leading to pulmonary emphysema, partly due to an oxidative inactivation of alpha 1 antiprotease inhibitor. In spite of additive effect of tobacco smoke, as shown by longitudinal studies of lung function in coal miners, it is now reasonable to consider the evidence of association between coal mine exposure and COPD to be sufficiently strong to infer causality, and to justify compensation for COPD in coal miners with or without radiological evidence of pneumoconiosis.

Coal↗
PubMed2004

[Evaluative research and epidemiology: trends and synthesis in the health programs evaluation process].

This article focuses on antecedents in the evaluation of health programs and highlights the diversity of theories and methods applied to the field. In order to provide support for evaluative research in HIV/AIDS, the article discusses several classical principles in epidemiology, particularly the theory of causal inference and its limits for approaching infectious diseases. The article contends that recent debates concerning the logic and applicability of ecological studies establish convergent interests between epidemiology and evaluative research, concerned with incorporating contextual variables into the analytical models. The principles and typologies of contemporary evaluative research are explained with a view towards defending an analytical model for health-disease processes from the perspective of evaluating actions and programs. The article concludes by highlighting common proposals and convergent trends in epidemiology and evaluative research, serving as the framework for discussion on the limits and potentialities of the proposed model.

Acquired Immunodeficiency Syndrome↗
PubMed2000

Cluster inference methods and graphical models evaluated on NCI60 microarray gene expression data.

At present, there is a lack of a sound methodology to infer causal gene expression relationships on a genome wide basis. We address this first by examining the behaviour of some of the latest and fastest algorithms for tree and cluster analysis, particularly hierarchical methods popular in phylogenetics. Combined with these are two novel distances based on partial, rather than full, correlations. Theoretically, partial correlations should provide better evidence for regulatory genetic links than standard correlations. To compare the clusters obtained by many alternative methods we use tree consensus methods. To compare methods of analysis we used tree partition metrics followed by another level of clustering. These, and a tree fit metric, all suggest that the new distances give quite different trees than those usually obtained. In the second part we consider graphical modeling of the interactions of important genes of the cell cycle. Despite the models seeming to fit well on occasions, and despite the experimental error structure seeming close to multivariate normal, there are considerable problems to overcome. Latent variables, in this case important genes missing from the analysis, are inferred to have a strong effect on the partial correlations. Also, the data show clear evidence of sampling distributions conditional on the status of important cancer related genes, including TP53. Without full information on which genes are wild type the appropriate models cannot be fitted. These findings point to the need to include and distinguish not only all relevant genes but also all splice variants in the design phase of a microarray analysis. Failure to do so will induce problems similar to both latent variables and conditional distributions.

Algorithms↗
PubMed1994

Social factors and health: the causation-selection issue revisited.

Since social scientists rarely have access to experimental data, they rely heavily upon observational studies. As a consequence, their attempts to make causal inferences about the effects of social factors--such as occupation or marital status--on health are plagued by potential selection problems. Some researchers have addressed this selection-causation problem on the basis of the presence or absence of a particular aggregate pattern of health status. The rationale underlying this approach derives from the investigators' hypotheses that the presence of selection would lead to a particular type of pattern that is distinct from the pattern that would result in the absence of selection. Although intuitively appealing, this strategy appears to be seriously flawed. The essential weakness is that the range of patterns that can result from selection is often much broader than researchers have speculated.

Causality↗
PubMed2026

Mitochondria-Related Pathogenic Genes in Paediatric Asthma: A Multi-Omics Mendelian Randomization Study.

Mitochondrial dysfunction is implicated in asthma pathogenesis, but causal roles of mitochondrial-related genes in paediatric asthma remain unclear. We performed a multi-omics Mendelian randomization study integrating GWAS data from paediatric asthma cohorts with blood-based methylation quantitative trait loci (mQTLs), expression QTLs (eQTLs) and protein QTLs (pQTLs) datasets. Causal inference was assessed using Summary-data-based Mendelian Randomization (SMR) and HEIDI testing, complemented by colocalization analysis. Findings were validated in independent cohorts and evaluated for tissue specificity using GTEx. Functional enrichment and protein-protein interaction (PPI) network analyses were conducted. SMR analysis identified 80 methylation sites spanning 54 genes, 26 gene expressions, and three proteins significantly associated with paediatric asthma. Colocalization analysis confirmed strong evidence for 10 methylation sites (7 genes), the STX17 eQTL (PP.H4 = 0.98) and the UNG pQTL (PP.H4 = 0.84). Tissue-specific eQTL validation replicated the STX17 association. Multi-omics integration associated ALAS1 (cg13241645, cg15698299) and TXNRD1 (cg09884423) with asthma at both methylation and expression levels, with colocalization supporting both ALAS1 associations. Furthermore, integrated mQTL-eQTL analysis suggests that DNA methylation potentially regulates ALAS1 and TXNRD1 expression. Functional enrichment and network analyses revealed that these candidate genes converge on mitochondrial metabolic pathways and identified seven hub genes with potential regulatory significance (SDHB, MFN2, GLDC, PHB2, TXNRD1, ATP5MC1 and PHB). This study provides multi-omics evidence supporting a causal role for mitochondrial-related genes, particularly ALAS1 and TXNRD1, in paediatric asthma, offering new insights into pathogenesis and potential therapeutic targets.

Humans↗
PubMed2026

Mendelian randomisation for rheumatology: beyond hype-what it's good for, what it can't do, and how to read it critically.

Mendelian randomisation (MR) has become abundant in the literature, with variation in quality and frequent overinterpretation of causality. This creates a problem for clinical readers, reviewers, and editors: some MR studies can sharpen causal thinking, prioritise drug targets, and challenge misleading observational claims, whereas others are little more than automated exposure-outcome scans with causal claims disproportionate to the evidence. MR can strengthen causal inference when randomised trials are impractical and conventional observational studies are vulnerable to confounding, reverse causation, or selection bias. In rheumatology, credible MR can contribute to questions about disease aetiology, modifiable risk factors, therapeutic target validation, adverse-effect anticipation, and phenotype validation. However, its interpretation depends on whether the exposure is plausibly instrumentable, whether the genetic instruments are biologically defensible, whether assumptions are interrogated in ways appropriate to the design, and whether findings are triangulated with clinical, observational, experimental, and mechanistic evidence. Instead of recapitulating all methodological issues of MR, this review aims to help rheumatologists distinguish robust MR from weak or overinterpreted analyses quickly. We provide an accessible framework for reading and triaging MR studies in rheumatology. Papers that use poorly justified instruments, treat medication use as drug-target evidence, interpret genetic liability as diagnosis, rely on mechanical sensitivity analyses, ignore prior evidence or ask no clinically meaningful question can often be passed over by readers. The goal is not to discourage MR in rheumatology, but to raise the standard; useful MR should clarify causal reasoning rather than simply generate another statistically significant association.

Journal Article↗
PubMed2005

Statistical issues in life course epidemiology.

There is growing recognition that the risk of many diseases in later life, such as type 2 diabetes or breast cancer, is affected by adult as well as early-life variables, including those operating prior to conception and during the prenatal period. Most of these risk factors are correlated because of common biologic and/or social pathways, while some are intrinsically ordered over time. The study of how they jointly influence later ("distal") disease outcomes is referred to as life course epidemiology. This area of research raises several issues relevant to the current debate on causal inference in epidemiology. The authors give a brief overview of the main analytical and practical problems and consider a range of modeling approaches, their differences determined by the degree with which associations present (or presumed) among the correlated explanatory variables are explicitly acknowledged. Standard multiple regression (i.e., conditional) models are compared with joint models where more than one outcome is specified. Issues arising from measurement error and missing data are addressed. Examples from two cohorts in the United Kingdom are used to illustrate alternative modeling strategies. The authors conclude that more than one analytical approach should be adopted to gain more insight into the underlying mechanisms.

Causality↗
PubMed1996

Bias or biology: evaluating the epidemiologic studies of L-tryptophan and the eosinophilia-myalgia syndrome.

When investigating the cause of a chronic disease, epidemiologists are forced to substitute observational, nonexperimental methods such as cohort or case-control studies for the scientifically preferred "gold standard," the randomized controlled trial. Because neither cohort nor case-control studies are done under experimental conditions, the results may not accurately reflect those that would be found in a randomized experiment. Before the results of epidemiologic research can be used for inference regarding a cause of disease, it is necessary to examine the design and conduct of such studies to ensure their results could not have been distorted by potential sources of bias. We evaluate the epidemiologic studies of the relationship between intake of L-tryptophan (LT) and the occurrence of eosinophilia-myalgia syndrome (EMS) from information provided in the published reports and underlying data and documentation of the studies obtained from the US Centers for Disease Control and state health departments. We reviewed separately the initial 2 studies that examined the link between LT and the risk for EMS and the subsequent studies that focused on the specific manufacturing process or chemical constitutents of LT that might have been responsible for EMS. The 2 initial studies compared cases of EMS with controls who were asymptomatic. The investigators concluded that ingestion of LT was associated with the occurrence of EMS. However, these studies contained methodologic problems, including diagnostic bias, publicity, recall and reporting bias, bias in the inclusion and exclusion of cases and controls, inequalities between cases and controls in the indications for the use of LT, and failure to ensure that the exposure to LT preceded the onset of symptoms of EMS. These potential biases make it difficult to use the data derived from these investigations to justify a causal inference. Subsequent studies were conducted under the assumption that there was a valid association between ingestion of LT and the occurrence of EMS. These studies focused on tracing back LT to the manufacturer. These studies also had a variety of methodologic shortcomings, including problems in the assembly of study subjects leading to the selected samples of cases and controls, the lack of information on the brand and lot of LT for many subjects, multiple brand use, differences in the timing of exposure between cases and controls, the difficulty of the process of tracing LT products to the appropriate manufacturer, inconsistent classification of symptoms depending on brand of LT used, and inconsistencies in the traceback procedures between cases and controls. In light of these analyses, it appears that the cause of EMS has not been definitively identified. The search for the cause of EMS should continue without the underlying assumption that LT or some contaminant is responsible.

Case-Control Studies↗
PubMed1997

Lead exposure and children's intelligence: do low levels of lead in blood cause mental deficit?

OBJECTIVE: It has come to be generally accepted that low levels of lead exposure may result in mental deficit. This causal inference is based on claimed time precedence of the lead exposure and on biological plausibility. The objective of this study is to argue that mental deficit causes pica which causes lead exposure (i.e. to support the theory of reverse causation). METHODOLOGY: The literature since the 1930s has been interpreted in the light of our own long experience in the investigation of lead exposure in children and adults to support the arguments in favour of reverse causation. RESULTS: The arguments for reverse causation are based on: (i) analogy with mental retardation which causes increased lead exposure; (ii) the results of published prospective studies that show a special relationship between blood lead levels at 24 months and intelligence tested later, exactly what would be predicted by the reverse causation theory; and (iii) on an alternative explanation for mental retardation following lead encephalopathy (i.e. that mental retardation following encephalopathy is due to anoxia and not due to a direct destructive effect on the brain neurones). The arguments, which have been proposed for the conventional view, are rejected for the following reasons: (i) none of the prospective studies have found a relationship between cord blood lead levels and intelligence tested later, undermining the argument based on time precedence of lead exposure; and (ii) there is no convincing evidence that lead poisoning, short of encephalopathy, causes mental retardation. CONCLUSION: We believe that the reverse causation hypothesis is a more plausible explanation of the facts.

Causality↗
PubMed2000

Depression is a risk factor for noncompliance with medical treatment: meta-analysis of the effects of anxiety and depression on patient adherence.

BACKGROUND: Depression and anxiety are common in medical patients and are associated with diminished health status and increased health care utilization. This article presents a quantitative review and synthesis of studies correlating medical patients' treatment noncompliance with their anxiety and depression. METHODS: Research on patient adherence catalogued on MEDLINE and PsychLit from January 1, 1968, through March 31, 1998, was examined, and studies were included in this review if they measured patient compliance and depression or anxiety (with n>10); involved a medical regimen recommended by a nonpsychiatrist physician to a patient not being treated for anxiety, depression, or a psychiatric illness; and measured the relationship between patient compliance and patient anxiety and/or depression (or provided data to calculate it). RESULTS: Twelve articles about depression and 13 about anxiety met the inclusion criteria. The associations between anxiety and noncompliance were variable, and their averages were small and nonsignificant. The relationship between depression and noncompliance, however, was substantial and significant, with an odds ratio of 3.03 (95% confidence interval, 1.96-4.89). CONCLUSIONS: Compared with nondepressed patients, the odds are 3 times greater that depressed patients will be noncompliant with medical treatment recommendations. Recommendations for future research include attention to causal inferences and exploration of mechanisms to explain the effects. Evidence of strong covariation of depression and medical noncompliance suggests the importance of recognizing depression as a risk factor for poor outcomes among patients who might not be adhering to medical advice.

Adult↗