The surprising implications of familial association in disease risk

The built-in selection bias of hazard ratios formalized using structural causal models

It is known that the hazard ratio lacks a useful causal interpretation. Even for data from a randomized controlled trial, the hazard ratio suffers from so-called built-in selection bias as, over time, the individuals at risk among the exposed and unexposed are no longer exchangeable. In this paper, we formalize how the expectation of the observed hazard ratio evolves and deviates from the causal effect of interest in the presence of heterogeneity of the hazard rate of unexposed individuals (frailty) and heterogeneity in effect (individual modification). For the case of effect heterogeneity, we define the causal hazard ratio. We show that the expected observed hazard ratio equals the ratio of expectations of the latent variables (frailty and modifier) conditionally on survival in the world with and without exposure, respectively. Examples with gamma, inverse Gaussian and compound Poisson distributed frailty and categorical (harming, beneficial or neutral) distributed effect modifiers are presented for illustration. This set of examples shows that an observed hazard ratio with a particular value can arise for all values of the causal hazard ratio. Therefore, the hazard ratio cannot be used as a measure of the causal effect without making untestable assumptions, stressing the importance of using more appropriate estimands, such as contrasts of the survival probabilities.

Heterogeneity in coronary heart disease risk

There is large inter-individual heterogeneity in risk of coronary heart disease (CHD). Risk factors traditionally used in primary risk assessment only partially explain this heterogeneity. Residual, unobserved heterogeneity leads to age-related attenuation of hazard rates and underestimation of hazard ratios. Its magnitude is unknown. Therefore, we aimed to estimate a lower and an approximate upper bound. Heterogeneity was parametrized by a log-normal distribution with shape parameter σ. Analysis was based on published data. From concordance indices of studies including traditional risk factors and additional diagnostic imaging data, we calculated the part of heterogeneity explained by imaging data. For traditional risk assessment, this part typically remains unexplained, thus constituting a lower bound on unobserved heterogeneity. Next, the potential impact of heterogeneity on CHD hazard rates in several large countries was investigated. CHD rates increase with age but the increase attenuates with age. Presuming this attenuation to be largely caused by heterogeneity, an approximate upper bound on σ was derived. Taking together both bounds, unobserved heterogeneity in studies without imaging information can be described by a shape parameter in the range σ = 1-2. It substantially contributes to observed age-dependences of hazard ratios and may lead to underestimation of hazard ratios by a factor of about two. Therefore, analysis of studies for primary CHD risk assessment should account for unobserved heterogeneity.

Expression of miR-451a in Prostate Cancer and Its Effect on Prognosis

Background:

To investigate the expression of miR-451a in prostate cancer tissues and its effect on prognosis.

Methods:

Each of 78 specimens of prostate cancer tissues and corresponding adjacent normal tissues were collected from patients in Changshu Hospital Affiliated to Soochow University, Changshu, China from Apr 2014 to Jun 2015. Real-time quantitative RT-PCR (qRT-PCR) was used to detect the expression of miR-451a in tissues. The relationship between the expression of miR-451a and clinical pathological parameters was analyzed. The median expression of miR-451a in the experimental group was used to distinguish the high and low expressions of miR-451a in the experimental group. Kaplan-Meier was used to analyze the survival of miR-451a high and low expression groups.

Results:

The expressions of miR-451a in the patient’s tissues and serum were decreased, and the correlation analysis found that they were positively correlated. ROC curve analysis showed that miR-451a had a high clinical value in the diagnosis of prostate cancer and the area under the curve was 0.921. The incidence of stage III+IV lymph node metastasis, Gleason score of >7 points and a serum Prostate-specific antigen (PSA) level of >20 ng/ml in patients of the low expression group increased significantly. The 5-yr survival rate of patients with low expression was significantly lower than that of those with high expression (P=0.005). MiR-451a was an independent factor affecting the prognosis of patients.

Conclusion:

miR-451a is lowly expressed in prostate cancer, and patients with low expression have a poor prognosis.

Sensing eating mimicry among family members

Family relationships influence eating behavior and health outcomes (e.g., obesity). Because eating is often habitual (i.e., automatically driven by external cues), unconscious behavioral mimicry may be a key interpersonal influence mechanism for eating within families. This pilot study extends existing literature on eating mimicry by examining whether multiple family members mimicked each other's bites during natural meals. Thirty-three participants from 10 families were videotaped while eating an unstructured family meal in a kitchen lab setting. Videotapes were coded for participants' bite occurrences and times. We tested whether the likelihood of a participant taking a bite increased when s/he was externally cued by a family eating partner who had recently taken a bite (i.e., bite mimicry). A paired-sample t-test indicated that participants had a significantly faster eating rate within the 5 s following a bite by their eating partner, compared to their bite rate at other times (t = 7.32, p < .0001). Nonparametric permutation testing identified five of 78 dyads in which there was significant evidence of eating mimicry; and 19 of 78 dyads that had p values < .1. This pilot study provides preliminary evidence that suggests eating mimicry may occur among a subset of family members, and that there may be types of family ties more prone to this type of interpersonal influence during meals.

Analysis of 153 115 patients with hematological malignancies refines the spectrum of familial risk

Estimating familial cancer risks is clinically important in being able to discriminate between individuals in the population at differing risk for malignancy. To gain insight into the familial risk for the different hematological malignancies and their possible inter-relationship, we analyzed data on more than 16 million individuals from the Swedish Family-Cancer Database. After identifying 153 115 patients diagnosed with a primary hematological malignancy, we quantified familial relative risks (FRRs) by calculating standardized incident ratios (SIRs) in 391 131 of their first-degree relatives. The majority of hematological malignancies showed increased FRRs for the same tumor type, with the highest FRRs being observed for mixed cellularity Hodgkin lymphoma (SIR, 16.7), lymphoplasmacytic lymphoma (SIR, 15.8), and mantle cell lymphoma (SIR, 13.3). There was evidence for pleiotropic relationships; notably, chronic lymphocytic leukemia was associated with an elevated familial risk for other B-cell tumors and myeloproliferative neoplasms. Collectively, these data provide evidence for shared etiological factors for many hematological malignancies and provide information for identifying individuals at increased risk, as well as informing future gene discovery initiatives.

Inequality in genetic cancer risk suggests bad genes rather than bad luck

Heritability is often estimated by decomposing the variance of a trait into genetic and other factors. Interpreting such variance decompositions, however, is not straightforward. In particular, there is an ongoing debate on the importance of genetic factors in cancer development, even though heritability estimates exist. Here we show that heritability estimates contain information on the distribution of absolute risk due to genetic differences. The approach relies on the assumptions underlying the conventional heritability of liability model. We also suggest a model unrelated to heritability estimates. By applying these strategies, we describe the distribution of absolute genetic risk for 15 common cancers. We highlight the considerable inequality in genetic risk of cancer using different metrics, e.g., the Gini Index and quantile ratios which are frequently used in economics. For all these cancers, the estimated inequality in genetic risk is larger than the inequality in income in the USA.