Abstract
In this issue of the Journal, Upson et al. (Am J Epidemiol. 2021;190(1):116–124) assess urinary cadmium level as a potential environmental influence on ovarian reserve, as measured using ...serum follicle-stimulating hormone, in data from 1,681 US women (1988–1994). They compare 3 methods for modeling urinary proxy exposures—standardization, covariate adjustment, and covariate-adjusted standardization. Observing positive associations with all 3 approaches but higher-magnitude estimates using covariate adjustment as compared with standardization and covariate-adjusted standardization—proposed to be the result of collider-stratification bias—the authors conclude that cadmium may affect ovarian aging, and they recommend careful consideration of modeling approach. Comparisons of methodology in practice using real data are not straightforward, and additional complication arises from using a proxy outcome—serum follicle-stimulating hormone level to represent diminished ovarian reserve. In this commentary, I describe the theoretical basis for approaches for modeling urinary proxy exposures; consider potential explanations for why the approaches may yield different results in practice and describe why measurement error may play a larger role than collider-stratification bias; discuss challenges related to studies of ovarian reserve; and emphasize the importance of addressing both theoretical concerns and real-world challenges in methodological research and epidemiologic studies of ovarian reserve.
Gestational diabetes mellitus (GDM) is glucose intolerance with first onset during pregnancy and is associated with serious maternal and fetal complications. The etiology of GDM is not well ...understood, but systemic inflammation effects on insulin signaling and glucose metabolism is suspected. Periodontal disease is a chronic inflammatory condition that induces local and host immune responses and has been evaluated for a potential role in development of GDM. Results from studies evaluating the association between periodontitis and GDM are mixed. We performed a systematic review and meta-analysis to summarize available data regarding the association between periodontitis and GDM.
Twelve electronic databases were searched for observational studies of the association between periodontitis and GDM through March 2016. Eligible studies were assessed for quality and heterogeneity. Random effects models were used to estimate summary measures of association.
We identified 44 articles from 115 potentially relevant reports of which 10 studies met our eligibility criteria. Clinical diagnostic criteria for periodontitis and GDM varied widely among studies, and moderate heterogeneity was observed. Random effects meta-analysis of all included studies with a total of 5724 participants including 624 cases, showed that periodontitis is associated with an increased risk of GDM by 66 %, (OR = 1.66, 95 % CI: 1.17 to 2.36; p < 0.05), I
= 50.5 %. Similar results were seen in sub-analysis restricted to data from methodologically high quality case-control studies including 1176 participants including 380 cases, (OR = 1.85, 95 % CI: 1.03 to 3.32); p < 0.05), I
= 68.4 %. Meta-analysis of studies that adjusted for potential confounders estimated more than 2-fold increased odds of GDM among women with periodontitis (aOR = 2.08, 95 % CI: 1.21 to 3.58, p = 0.009, I
= 36.9 %).
Meta-analysis suggests that periodontitis is associated with a statistically significant increased risk for GDM compared to women without periodontitis. Robust prospective study designs and uniform definition for periodontitis and GDM definitions are urgently needed to substantiate these findings.
Methods for causal inference have experienced tremendous recent growth; this paper introduces key concepts underlying causal effect methods in epidemiologic research. “Causal effects” can be ...described using counterfactuals, which consider alternate versions of reality, and have a long history of use for interpreting cause-effect relationships in terms of what would have happened had things (e.g., exposures) been one way vs. another. Despite their intuitive appeal for thinking about causality, use of counterfactuals in formal causal inference requires attention to potential limitations. For example, consider a study of the effect of smoking on the 5-year cumulative risk of cardiovascular disease (CVD). Because of ambiguity in the “effect of smoking,” the corresponding counterfactual contrast is unclear and could correspond to a comparison between risk that would have happened if all study participants had ever smoked vs. never smoked, smoked 3 vs. 2 cigarettes per day, continued to smoke vs. quitting, and so on (1). There is an important degree of ambiguity in the language we use informally to ask causal questions. Causal inference is largely devoted to clarifying this ambiguity through use of potential outcomes as a formal framework, and a set of assumptions to allow interpretation of associations causally.
Gestational diabetes mellitus (GDM) is a common complication of pregnancy associated with an increased incidence of pregnancy complications, adverse pregnancy outcomes, and maternal and fetal risks ...of chronic health conditions later in life. Physical activity has been proposed to reduce the risk of GDM and is supported by observational studies, but experimental research assessing its effectiveness is limited and conflicting. We aimed to use meta-analysis to synthesize existing randomized controlled studies of physical activity and GDM.
We searched MEDLINE, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov for eligible studies.
The following combination of keywords was used: (pregnant or pregnancy or gestation or gestate or gestational or maternity or maternal or prenatal) AND (exercise or locomotion or activity or training or sports) AND (diabetes or insulin sensitivity or glucose tolerance) AND (random* or trial). Eligibility was restricted to studies that randomized participants to an exercise-only-based intervention (ie, separate from dietary interventions) and presented data regarding GDM risk. Two authors performed the database search, assessment of eligibility, and abstraction of data from included studies, and a third resolved any discrepancies. A total of 469 studies was retrieved, of which 10 met inclusion criteria and could be used for analysis (3,401 participants).
Fixed-effects models were used to estimate summary relative risk (RR) and 95% confidence interval (CI) and I to assess heterogeneity. There was a 28% reduced risk (95% CI 9-42%) in the intervention group compared with the control group (RR 0.72, P=.005). Heterogeneity was low (I=12%) and nonsignificant (P=.33).
The results from this meta-analysis suggest that physical activity in pregnancy provides a slight protective effect against the development of GDM. Studies evaluating type, timing, duration, and compliance of physical activity regimens are warranted to best inform obstetric guidelines.
Correlated data refers to a situation where the outcome of interest is clustered within a particular grouping, and they are very common in epidemiology and public health research. Here, we discuss ...situations that lead to, and complications that result from, correlated data. We demonstrate 2 simple strategies that can be used to analyze correlated data and still obtain valid inferences.
The concept of interaction in etiological epidemiologic research can be described as the circumstance where the causal effect of exposure on outcome depends upon another factor. In this sense, ...epidemiologic research commonly considers interaction,. For example: How is risk of lung cancer affected by cigarette smoking and asbestos separately and in combination? Is effectiveness of selective serotonin reuptake inhibitors for treatment of unipolar depression dependent upon patient characteristics? Note that we largely use the terms “interaction” and “effect modification” interchangeably. These are sometimes distinguished from one another, as when there is similar interest in 2 exposures vs. 1 exposure of primary interest with effects that may vary across subgroups.More generally, consider a hypothetical study of dichotomous outcome Y and dichotomous factors A and B, aimed at evaluating whether the effect of A on risk of Y among those with B is different among those without B. Similarly, we can consider risk related to the combination of A and B together instead of subgroup-specific effects of A, to evaluate whether risk among those with both A and B is greater or less than expected based on effects of A and B individually—which we could describe qualitatively as “synergism” or “antagonism” (1, 2). The vague descriptions above are adequate to describe the concept of epidemiologic interaction. But progressing from vague descriptions of interaction to formal evaluation requires resolving ambiguity about the meanings of “the effect” in the phrase “the effect of A on risk of Y,” and “expected” in the phrase “greater or less than expected.”
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