Methodologic Implications of the Precautionary Principle: Causal Criteria

Applying the Precautionary Principle to public health requires a re-evaluation of the methods of inference currently used to make claims about disease causation from epidemiologic and other forms of scientific evidence. In current thinking, a well-established, near-certain causal relationship implies highly consistent statistically significant results across many different studies, large relative risk estimates, extensive understanding of biological mechanisms and dose-response relationships, positive prevention trial results, a clear temporal relationship between cause and effect, and other conditions spelled out in terms of the widely-used causal criteria. The Precautionary Principle, however, states that preventive measures are to be taken when cause and effect relationships are not fully established scientifically. What evidentiary conditions, as reflected in the causal criteria, will be certain enough to warrant precautionary preventive action? This paper argues that minimum evidentiary requirements for causation need to be articulated if the Precautionary Principle is to be successfully incorporated into public health practice. Two precautionary changes to criteria-based methods of causal inference are examined: reducing the number of criteria and weakening the rules of inference accompanying the criteria. Such changes point in the direction of identifying minimum evidentiary conditions, but would be premature without better understanding how well current methods of causal inference work.     

Toward a broader notion of community

Both leading scientific journals and the popular press now regularly report the convincing evidence of massive environmental degradation and decline. Yet despite the seriousness of the problems, despite their anthropogenic nature, and despite their profound implications for present and future population health, such topics are rarely discussed in the leading public health journals. When these issues are mentioned, they are examined in the same limited framework as other questions in public health--questions of models and tests of independent causal associations dominate. This approach will not suffice, for both scientific and ethical reasons. If public health scientists wish to sustain human health in the face of such crises, and to retain our integrity as scholars who speak truthfully about public health matters, we will have to broaden the notions of "health" and "community" to include nonhumans. I draw on recent scholarship in moral philosophy and in the philosophy of science to support my argument. Scholars in the health professions must take seriously the words of theologian Andrew Linzey, who states that the attempt to place human well-being in a special and absolute category of its own is perhaps the primary cause of our ecological travail.     

The Sustainability Balanced Scorecard as a Framework for Eco-efficiency Analysis

To provide valuable support for successful decision-making, managers need a balanced set of financial and nonfinancial measures that represent different requirements, strategic goals, strategies, resources, and capabilities and the causal relationships between these domains. The balanced scorecard is such a measurement system. As an open system the balanced scorecard facilitates the consideration of sustainability issues. But enhanced balanced scorecards require a new type of data. This is where eco-efficiency analysis comes into play.
This article discusses the relationship between so-called sustainability balanced scorecards and eco-efficiency analysis. Eco-efficiency analysis not only provides a data source for sustainability balanced scorecards; in the perspective of environmental information systems it also serves as a link between the balanced scorecard and corporate environmental accounting systems so that eco-efficiency as a component of an environmental information system becomes an adapter with two interfaces, which are characterized in this article. The main focus is on the principle of cause and effect, its different forms, and the implications for the design of appropriate information system components.     

Of babies and birds: complex tool behaviours are not sufficient for the evolution of the ability to create a novel causal intervention

Humans are capable of simply observing a correlation between cause and effect, and then producing a novel behavioural pattern in order to recreate the same outcome. However, it is unclear how the ability to create such causal interventions evolved. Here, we show that while 24-month-old children can produce an effective, novel action after observing a correlation, tool-making New Caledonian crows cannot. These results suggest that complex tool behaviours are not sufficient for the evolution of this ability, and that causal interventions can be cognitively and evolutionarily disassociated from other types of causal understanding.     

Combining environmental exposure and genetic effect measurements in health outcome assessment.

The presence of overwhelming difficulties in assessing the extent or even the presence of a causal association between modern environmental exposures and disease has promoted the use of more complex models in the design of human biomonitoring studies. The concatenation of environmental exposure, genetic effect and individual susceptibility is a key issue in the assessment of risks for populations exposed to environmental pollutants. The use of a biological event laying in the causal pathway from exposure to outcome as surrogate end-point of disease, can potentially anticipate clinical diagnosis, offering a number of possibilities for application of preventive measures. Numerous biomarkers are currently employed to study human populations exposed to environmental carcinogens, among these, the frequency of chromosomal aberration (CA) in peripheral blood lymphocytes has the most abundant literature linking a genetic effect with the occurrence of cancer. Findings from recent epidemiological studies which have followed-up a large group of healthy subjects screened for CAs have lent further support to the use of chromosomal breakage as a relevant biomarker of cancer risk. The applicability of surrogate end-points of cancer on an individual basis thus far seems to be limited to few examples. On the other hand, from a public health outlook, increases in the frequency of surrogate end-points are suggestive of an increased risk of cancer, and for validated biomarkers such as CAs intervention policies and actions in exposed populations showing increased frequency of these end-points should be always recommended.     

Establishing Causality between Environmental Stressors and Effects on Aquatic Ecosystems

Establishing causal relationships between environmental stressors and observed effects in natural systems is difficult due to the many intrinsic environmental factors that can hinder this process and because there are no widely accepted and proven approaches for determining such relationships. Several types of approaches or combinations of approaches, each with their own sets of advantages and limitations, have been applied in a variety of ecological systems to investigate possible causal relationships between stressors and effects. These include controlled laboratory studies (including acute and chronic bioassays), experimental field manipulations, field studies based on synoptic field surveys, mathematical simulation modeling, statistical associations, various combinations of laboratory, experimental, and field studies, and the ecoepidemiological (weight or evidence) approach. The use of ecoepidemiological (“forensic toxicology”) principles is becoming increasingly attractive as a method to help establish causality because it does not involve the same limitations of other approaches and it can also be used to integrate disparate information within a logical framework so that scientifically and defensible regulatory decisions can be made. The objective of this Commentary series of papers on the issue on causality is to demonstrate the application of the ecoepidemiology approach, using a variety of case history studies, for establishing causal relationships between specific stressors and biological effects. For each case history provided in the following series of papers, the authors describe their study situation, summarize the results supporting a causal relationship, and then compare their study results against seven standard causal criteria.     

What Biomonitoring Can and Cannot Tell Us about Causality in Human Health and Ecological Risk Assessments

Biomonitoring can provide exposure and effects information on various stressors (chemical or biological) that can be useful for human health and ecological risk assessments. It has been applied over the years where harmful changes in human health or the environment were observed and which may have warranted more detailed investigation. Sometimes biomonitoring programs may have been useful in determining the significance and/or cause of these harmful observations. These data can help to infer, but not confirm, causality as exemplified in classical studies conducted in humans and wildlife. However, in most cases we note that additional work was needed to provide the information necessary to support or refute causality. Today modern technology provides the ability to measure a wide variety of parameters in environmental media, plants, animals, and humans. Finding a chemical in an environmental medium or biological tissue may be helpful in understanding potential exposure (and perhaps to begin estimating hazard) to humans and ecological receptors, but mere presence does not necessarily help to establish effects or assign causality. In this article we evaluate the strengths and weaknesses, in a risk assessment context, of the use of biomonitoring data to support a determination of causality.     

Genetic influences on lipid metabolism trait variability within the Stanislas Cohort.

The contribution of 17 polymorphisms within 13 candidate genes on lipid trait variability was investigated by a multiplex assay in 772 men and 780 women coming for a health checkup examination. The studied genes were APOE, APOB, APOC3, CETP, LPL, PON, MTHFR, FGB, GpIIIa, SELE, ACE, and AGT. We found that APOB-Thr71Ile, APOE-(112/158), APOC3-1100C/T, and SELE-98G/T polymorphisms had a significant effect on lipid traits (P < or = 0.001 to P < or = 0.01). Genetic effects accounted for 3.5-5.7% of variation in apolipoprotein B (apoB)-related traits among men, and for 5.7-9.0% among women. The contribution of APOE polymorphism on apoB-related traits variability was two to three times more important in women than in men. We found suggestive evidence for interactive effects between genetics and age, smoking status, and oral contraceptives. Increase of LDL-cholesterol and apoB concentrations with age was stronger among the epsilon4 carriers in women, and apolipoprotein A-I (apoA-I) concentration decreased with age in epsilon4 male carriers. The effect of epsilon2 allele on LDL-cholesterol was more important in the oral contraceptive users. In nonsmokers only, the APOC3-1100C allele in women was related to lower apoB-related traits concentrations, and in men to higher apoA-I and HDL-cholesterol concentrations. In conclusion, this work, in addition to the reinforcement of the already known associations between APOB, APOE, and APOC3 genes and lipids, leads to new perspectives in the complex relationships among genes and environmental factors. The newly observed relationships between E-selectine gene and lipid concentrations support the hypotheses of multiple metabolic pathways contributing to the complexity of lipids variability.     

A Novel Approach for Identifying Causal Models of Complex Diseases from Family Data

Causal models including genetic factors are important for understanding the presentation mechanisms of complex diseases. Familial aggregation and segregation analyses based on polygenic threshold models have been the primary approach to fitting genetic models to the family data of complex diseases. In the current study, an advanced approach to obtaining appropriate causal models for complex diseases based on the sufficient component cause (SCC) model involving combinations of traditional genetics principles was proposed. The probabilities for the entire population, i.e., normal–normal, normal–disease, and disease–disease, were considered for each model for the appropriate handling of common complex diseases. The causal model in the current study included the genetic effects from single genes involving epistasis, complementary gene interactions, gene–environment interactions, and environmental effects. Bayesian inference using a Markov chain Monte Carlo algorithm (MCMC) was used to assess of the proportions of each component for a given population lifetime incidence. This approach is flexible, allowing both common and rare variants within a gene and across multiple genes. An application to schizophrenia data confirmed the complexity of the causal factors. An analysis of diabetes data demonstrated that environmental factors and gene–environment interactions are the main causal factors for type II diabetes. The proposed method is effective and useful for identifying causal models, which can accelerate the development of efficient strategies for identifying causal factors of complex diseases.     

SCtool:识别复杂性状和疾病间遗传关联的工具

识别复杂性状和疾病间遗传关联可以提供有用的病因学见解,并有助于确定可能的因果关系的优先级。尽管已有很多工具可以实现复杂性状和疾病间遗传关联,但是某些工具代码可读性差、并且不同工具基于不同的计算机语言、工具间的串联性较差。因此,本研究基于全基因组关联研究(GWAS)数据,提出了SCtool,一个开源、跨平台和用户友好的软件工具。SCtool整合了ldsc, TwosampleMR和MR-BMA三种软件,其主要功能是基于GWAS汇总水平的数据,识别复杂性状和疾病、复杂性状和复杂性状以及疾病与疾病间的遗传相关性并探究其间潜在的因果关联。最后,使用SCtool揭示了全身性铁状态(铁蛋白,血清铁,转铁蛋白,转铁蛋白饱和度)与表观遗传时钟GrimAge之间的遗传关联。     

Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g., wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention’s effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.     

Effects of flower size and number on pollinator visitation to wild radish,Raphanus raphanistrum

Plant traits that increase pollinator visitation should be under strong selection. However, few studies have demonstrated a causal link between natural variation in attractive traits and natural variation in visitation to whole plants. Here we examine the effects of flower number and size on visitation to wild radish by two taxa of pollinators over 3 years, using a combination of multiple regression and experimental reductions in both traits. We found strong, consistent evidence that increases in both flower number and size cause increased visitation by syrphid flies. The results for small bees were harder to interpret, because the multiple regression and experimental manipulation results did not agree. It is likely that increased flower size causes a weak increase in small-bee visitation, but strong relationships between flower number and small-bee visitation seen in 2 years of observational studies were not corroborated by experimental manipulation of this trait. Small bees may actually have responded to an unmeasured trait correlated with flower number, or lower small-bee abundances when the flower number manipulation was conducted may have reduced our ability to detect a causal relationship. We conclude that studies using only 1 year, one method, or measuring only one trait may not provide an adequate understanding of the effects of plant traits on pollinator attraction.     

Causal measures of structure and plasticity in simulated and living neural networks

A major goal of neuroscience is to understand the relationship between neural structures and their function. Recording of neural activity with arrays of electrodes is a primary tool employed toward this goal. However, the relationships among the neural activity recorded by these arrays are often highly complex making it problematic to accurately quantify a network's structural information and then relate that structure to its function. Current statistical methods including cross correlation and coherence have achieved only modest success in characterizing the structural connectivity. Over the last decade an alternative technique known as Granger causality is emerging within neuroscience. This technique, borrowed from the field of economics, provides a strong mathematical foundation based on linear auto-regression to detect and quantify "causal" relationships among different time series. This paper presents a combination of three Granger based analytical methods that can quickly provide a relatively complete representation of the causal structure within a neural network. These are a simple pairwise Granger causality metric, a conditional metric, and a little known computationally inexpensive subtractive conditional method. Each causal metric is first described and evaluated in a series of biologically plausible neural simulations. We then demonstrate how Granger causality can detect and quantify changes in the strength of those relationships during plasticity using 60 channel spike train data from an in vitro cortical network measured on a microelectrode array. We show that these metrics can not only detect the presence of causal relationships, they also provide crucial information about the strength and direction of that relationship, particularly when that relationship maybe changing during plasticity. Although we focus on the analysis of multichannel spike train data the metrics we describe are applicable to any stationary time series in which causal relationships among multiple measures is desired. These techniques can be especially useful when the interactions among those measures are highly complex, difficult to untangle, and maybe changing over time.     

Epigenetics in disease: Leader or follower?

Epigenetic silencing is a pervasive mode of gene regulation in multicellular eukaryotes: stable differentiation of somatic cell types requires the maintenance of subsets of genes in an active or silent state. The variety of molecules involved, and the requirement for active maintenance of epigenetic states, creates the potential for errors on a large scale. When epigenetic errors - or epimutations - activate or inactivate a critical gene, they may cause disease. An epimutation that occurs in the germline or early embryo can affect all, or most, of the soma and phenocopy genetic disease. But the stochastic and reversible nature of epigenetic phenomena predicts that epimutations are likely to be mosaic and inherited in a nonmendelian manner; epigenetic diseases will thus rarely behave in the comfortably predictable manner of genetic diseases but will display variable expressivity and complex patterns of inheritance. Much phenotypic variation and common disease might be explained by epigenetic variation and aberration. The known examples of true epigenetic disease are at present limited, but this may reflect only the difficulty in distinguishing causal epigenetic aberrations from those that are merely consequences of disease, a challenge further extended by the impact of environmental agents on epigenetic mechanisms. The rapidly developing molecular characterization of epigenomes, and the new ability to survey epigenetic marks on whole genomes, may answer many questions about the causal role of epigenetics in disease; these answers have the potential to transform our understanding of human disease.     

A future perspective on neurodegenerative diseases: nasopharyngeal and gut microbiota

Neurodegenerative diseases are considered a serious life‐threatening issue regardless of age. Resulting nerve damage progressively affects important activities, such as movement, coordination, balance, breathing, speech and the functioning of vital organs. Reports on the subject have concluded that neurodegenerative disease can be caused by mutations of susceptible genes, alcohol consumption, toxins, chemicals and other unknown environmental factors. Although several diagnostic techniques can be used to determine aetiologies, the process is difficult and often fails. Research shows that nasopharyngeal and gut microbiota play important roles in brain to spinal cord coordination. However, no conclusive epidemiologic evidence is available on the roles played by respiratory and gut microbiota in the development of neurodegenerative diseases. Thus, understanding the connection between respiratory and gut microbiota and the nervous system could provide information on causal links. The present review describes future perspectives on the role played by nasopharyngeal and gut microbiota in the development of neurodegenerative diseases.     

Complexity,dynamics and diversity of sociality in group-living mammals

Numerous studies in group-living animals with stable compositions have demonstrated the complex and dynamic nature of social behaviour. Empirical studies occasionally provide principles that cannot be applied directly to other group-living species. Because of this, researchers are required to address fine-scaled conceptual questions and to incorporate species-specific characteristics of the study species. In this paper, I raise three key topics that will promote our understanding of animal sociality: the effects of heterogeneous social relationships on the pattern, distribution, and function of social interactions; conflict management for maintaining group living; and meta-dyad-level perspectives for understanding dyadic social relationships and behaviours. Through the discussion of these topics together with examples of group-living mammals, I emphasise the importance of direct behavioural observations and functional analyses in studies of species- or taxonomic-group-specific characteristics of social behaviour in a wide range of taxonomic groups. In addition to approaches focusing on specificity, another approach that examines the general principles or common characteristics found across different taxonomic groups could provide synthetic and reductive frameworks to understand divergent sociality. The complementary use of these two approaches will offer a comprehensive understanding of social evolution in group-living animals. Nobuyuki Kutsukake is the recipient of the 12th Denzaburo Miyadi Award.     

Mendelian randomization: can genetic epidemiology help redress the failures of observational epidemiology?

Establishing causal relationships between environmental exposures and common diseases is beset with problems of unresolved confounding, reverse causation and selection bias that may result in spurious inferences. Mendelian randomization, in which a functional genetic variant acts as a proxy for an environmental exposure, provides a means of overcoming these problems as the inheritance of genetic variants is independent of—that is randomized with respect to—the inheritance of other traits, according to Mendel’s law of independent assortment. Examples drawn from exposures and outcomes as diverse as milk and osteoporosis, alcohol and coronary heart disease, sheep dip and farm workers’ compensation neurosis, folate and neural tube defects are used to illustrate the applications of Mendelian randomization approaches in assessing potential environmental causes of disease. As with all genetic epidemiology studies there are problems associated with the need for large sample sizes, the non-replication of findings, and the lack of relevant functional genetic variants. In addition to these problems, Mendelian randomization findings may be confounded by other genetic variants in linkage disequilibrium with the variant under study, or by population stratification. Furthermore, pleiotropy of effect of a genetic variant may result in null associations, as may canalisation of genetic effects. If correctly conducted and carefully interpreted, Mendelian randomization studies can provide useful evidence to support or reject causal hypotheses linking environmental exposures to common diseases.     

The Importance of Spatial Effects for Environmental Health Policy and Research

Issues of spatial scale and resolution are intrinsic to efforts aimed at protecting and improving environmental health. Deciding on an appropriate policy or selecting a suitable research design implies a decision, either implicit or explicit, about spatial scale and resolution. This article looks at issues in the context of environmental health, reviews crucial problems and questions, and examines examples of spatial effects on analytical results related to causal inference, disease clustering, and analysis and interpretation of census data. The discussion focuses on the need to consider spatial issues as a key component of informed, well- reasoned decisions about safeguarding environmental health.