Relaxing parametric assumptions for non-linear Mendelian randomization using a doubly-ranked stratification method.

Non-linear Mendelian randomization is an extension to standard Mendelian randomization to explore the shape of the causal relationship between an exposure and outcome using an instrumental variable. A stratification approach to non-linear Mendelian randomization divides the population into strata and calculates separate instrumental variable estimates in each stratum. However, the standard implementation of stratification, referred to as the residual method, relies on strong parametric assumptions of linearity and homogeneity between the instrument and the exposure to form the strata. If these stratification assumptions are violated, the instrumental variable assumptions may be violated in the strata even if they are satisfied in the population, resulting in misleading estimates. We propose a new stratification method, referred to as the doubly-ranked method, that does not require strict parametric assumptions to create strata with different average levels of the exposure such that the instrumental variable assumptions are satisfied within the strata. Our simulation study indicates that the doubly-ranked method can obtain unbiased stratum-specific estimates and appropriate coverage rates even when the effect of the instrument on the exposure is non-linear or heterogeneous. Moreover, it can also provide unbiased estimates when the exposure is coarsened (that is, rounded, binned into categories, or truncated), a scenario that is common in applied practice and leads to substantial bias in the residual method. We applied the proposed doubly-ranked method to investigate the effect of alcohol intake on systolic blood pressure, and found evidence of a positive effect of alcohol intake, particularly at higher levels of alcohol consumption.

Linking the relation between gut microbiota and glucocorticoid-induced osteoporosis.

Osteoporosis (OP) is the most prevalent metabolic bone disease, characterized by the low bone mass and microarchitectural deterioration of bone tissue. Glucocorticoid (GC) clinically acts as one of the anti-inflammatory, immune-modulating, and therapeutic drugs, whereas the long-term use of GC may cause rapid bone resorption, followed by prolonged and profound suppression of bone formation, resulting in the GC-induced OP (GIOP). GIOP ranks the first among secondary OP and is a pivotal risk for fracture, as well as high disability rate and mortality, at both societal and personal levels, vital costs. Gut microbiota (GM), known as the "second gene pool" of human body, is highly correlated with maintaining the bone mass and bone quality, and the relation between GM and bone metabolism has gradually become a research hotspot. Herein, combined with recent studies and based on the cross-linking relationship between GM and OP, this review is aimed to discuss the potential mechanisms of GM and its metabolites on the OP, as well as the moderating effects of GC on GM, thereby providing an emerging thought for prevention and treatment of GIOP.

Using genetic association data to guide drug discovery and development: Review of methods and applications.

Evidence on the validity of drug targets from randomized trials is reliable but typically expensive and slow to obtain. In contrast, evidence from conventional observational epidemiological studies is less reliable because of the potential for bias from confounding and reverse causation. Mendelian randomization is a quasi-experimental approach analogous to a randomized trial that exploits naturally occurring randomization in the transmission of genetic variants. In Mendelian randomization, genetic variants that can be regarded as proxies for an intervention on the proposed drug target are leveraged as instrumental variables to investigate potential effects on biomarkers and disease outcomes in large-scale observational datasets. This approach can be implemented rapidly for a range of drug targets to provide evidence on their effects and thus inform on their priority for further investigation. In this review, we present statistical methods and their applications to showcase the diverse opportunities for applying Mendelian randomization in guiding clinical development efforts, thus enabling interventions to target the right mechanism in the right population group at the right time. These methods can inform investigators on the mechanisms underlying drug effects, their related biomarkers, implications for the timing of interventions, and the population subgroups that stand to gain the most benefit. Most methods can be implemented with publicly available data on summarized genetic associations with traits and diseases, meaning that the only major limitations to their usage are the availability of appropriately powered studies for the exposure and outcome and the existence of a suitable genetic proxy for the proposed intervention.