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Background Obesity is a worldwide epidemic that has been associated with a plurality of diseases in observational studies. The aim of this study was to summarize the evidence from Mendelian randomization (MR) studies of the association between body mass index (BMI) and chronic diseases. Methods PubMed and Embase were searched for MR studies on adult BMI in relation to major chronic diseases, including diabetes mellitus; diseases of the circulatory, respiratory, digestive, musculoskeletal, and nervous systems; and neoplasms. A meta-analysis was performed for each disease by using results from published MR studies and corresponding de novo analyses based on summary-level genetic data from the FinnGen consortium ( n = 218,792 individuals). Results In a meta-analysis of results from published MR studies and de novo analyses of the FinnGen consortium, genetically predicted higher BMI was associated with increased risk of type 2 diabetes mellitus, 14 circulatory disease outcomes, asthma, chronic obstructive pulmonary disease, five digestive system diseases, three musculoskeletal system diseases, and multiple sclerosis as well as cancers of the digestive system (six cancer sites), uterus, kidney, and bladder. In contrast, genetically predicted higher adult BMI was associated with a decreased risk of Dupuytren’s disease, osteoporosis, and breast, prostate, and non-melanoma cancer, and not associated with Alzheimer’s disease, amyotrophic lateral sclerosis, or Parkinson’s disease. Conclusions The totality of the evidence from MR studies supports a causal role of excess adiposity in a plurality of chronic diseases. Hence, continued efforts to reduce the prevalence of overweight and obesity are a major public health goal.

Dipender Gill and Stephen Burgess discuss the accompanying study by James Yarmolinsky and colleagues investigating the associations between genetically-proxied inhibition of antihypertensive drug targets and risk of common cancer subtypes using Mendelian randomization.

Modern high-throughput experiments provide a rich resource to investigate causal determinants of disease risk. Mendelian randomization (MR) is the use of genetic variants as instrumental variables to infer the causal effect of a specific risk factor on an outcome. Multivariable MR is an extension of the standard MR framework to consider multiple potential risk factors in a single model. However, current implementations of multivariable MR use standard linear regression and hence perform poorly with many risk factors. Here, we propose a two-sample multivariable MR approach based on Bayesian model averaging (MR-BMA) that scales to high-throughput experiments. In a realistic simulation study, we show that MR-BMA can detect true causal risk factors even when the candidate risk factors are highly correlated. We illustrate MR-BMA by analysing publicly-available summarized data on metabolites to prioritise likely causal biomarkers for age-related macular degeneration. Multivariable Mendelian randomization (MR) extends the standard MR framework to consider multiple risk factors in a single model. Here, Zuber et al. propose MR-BMA, a Bayesian variable selection approach to identify the likely causal determinants of a disease from many candidate risk factors as for example high-throughput data sets.

About half of the Amazon rainforest is subject to seasonal droughts of 3 months or more. Despite this drought, several studies have shown that these forests, under a strongly seasonal climate, do not exhibit significant water stress during the dry season. In addition to deep soil water uptake, another contributing explanation for the absence of plant water stress during drought is the process of hydraulic redistribution; the nocturnal transfer of water by roots from moist to dry regions of the soil profile. Here, we present data on patterns of soil moisture and sap flow in roots of three dimorphic-rooted species in the Tapajós Forest, Amazônia, which demonstrate both upward (hydraulic lift) and downward hydraulic redistribution. We measured sap flow in lateral and tap roots of our three study species over a 2-year period using the heat ratio method, a sap-flow technique that allows bi-directional measurement of water flow. On certain nights during the dry season, reverse or acropetal flow (i.e.,in the direction of the soil) in the lateral roots and positive or basipetal sap flow (toward the plant) in the tap roots of Coussarea racemosa (caferana), Manilkara huberi (maçaranduba) and Protium robustum (breu) were observed, a pattern consistent with upward hydraulic redistribution (hydraulic lift). With the onset of heavy rains, this pattern reversed, with continuous night-time acropetal sap flow in the tap root and basipetal sap flow in lateral roots, indicating water movement from wet top soil to dry deeper soils (downward hydraulic redistribution). Both patterns were present in trees within a rainfall exclusion plot (Seca Floresta) and to a more limited extent in the control plot. Although hydraulic redistribution has traditionally been associated with arid or strongly seasonal environments, our findings now suggest that it is important in ameliorating water stress and improving rain infiltration in Amazonian rainforests. This has broad implications for understanding and modeling ecosystem process and forest function in this important biome.

Background As sap flow research expands, new challenges such as fast sap flows or flows co-occurring with freeze/thaw cycles appear, which are not easily addressed with existing methods. In order to address these new challenges, sap flow methods capable of measuring bidirectional, high and slow sap flux densities ( F d , cm 3 cm −2  h −1 ), thermal properties and stem water content with minimum sensitivity to stem temperature are required. Purpose In this study we assessed the performance of a new low-power ratio-based algorithm, the maximum heat ratio ( MHR ) method, and compare it with the widely known heat ratio ( HR ) method using a cut-tree study to test it under high flows using Eucalyptus grandis trees, and a freeze/thaw experiment using Acer saccharum trunks to test its response to fast changing stem temperatures that result in freeze/thaw cycles. Results Our results indicate that MHR and HR had a strong (R 2  = 0.90) linear relationship within a F d range of 0–45 cm 3  cm −2  h −1 . Using the MHR algorithm, we were able to estimate wood thermal properties and water content, while extending the measuring range of HR to approximately 0–130 (cm 3 cm −2  h −1 ). In our freeze/thaw experiment, the main discrepancy between MHR and HR was observed during freezing, where HR had consistently lower F d (up to 10 cm 3  cm −2  h −1 ), with respect to MHR. However, both algorithms identified similar zero flows. Conclusion Consequently, MHR can be an easy-to-implement alternative algorithm/method capable of handling extreme climatic conditions, which can also run simultaneously with HR.

Background Genome-wide association studies have enabled Mendelian randomization analyses to be performed at an industrial scale. Two-sample summary data Mendelian randomization analyses can be performed using publicly available data by anyone who has access to the internet. While this has led to many insightful papers, it has also fuelled an explosion of poor-quality Mendelian randomization publications, which threatens to undermine the credibility of the whole approach. Findings We detail five pitfalls in conducting a reliable Mendelian randomization investigation: (1) inappropriate research question, (2) inappropriate choice of variants as instruments, (3) insufficient interrogation of findings, (4) inappropriate interpretation of findings, and (5) lack of engagement with previous work. We have provided a brief checklist of key points to consider when performing a Mendelian randomization investigation; this does not replace previous guidance, but highlights critical analysis choices. Journal editors should be able to identify many low-quality submissions and reject papers without requiring peer review. Peer reviewers should focus initially on key indicators of validity; if a paper does not satisfy these, then the paper may be meaningless even if it is technically flawless. Conclusions Performing an informative Mendelian randomization investigation requires critical thought and collaboration between different specialties and fields of research.

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.

Mendelian randomization (MR) is an epidemiological technique that uses genetic variants to distinguish correlation from causation in observational data. The reliability of a MR investigation depends on the validity of the genetic variants as instrumental variables (IVs). We develop the contamination mixture method, a method for MR with two modalities. First, it identifies groups of genetic variants with similar causal estimates, which may represent distinct mechanisms by which the risk factor influences the outcome. Second, it performs MR robustly and efficiently in the presence of invalid IVs. Compared to other robust methods, it has the lowest mean squared error across a range of realistic scenarios. The method identifies 11 variants associated with increased high-density lipoprotein-cholesterol, decreased triglyceride levels, and decreased coronary heart disease risk that have the same directions of associations with various blood cell traits, suggesting a shared mechanism linking lipids and coronary heart disease risk mediated via platelet aggregation. Mendelian randomization (MR) is a method for inferring causal relationships between risk factors and outcomes via associated genetic variants. Here, Burgess et al. develop the contamination mixture method which yields robust MR results in the presence of invalid instrumental variables and groups variants by their effect estimates.

Optimality theory states that whole-tree carbon gain is maximized when leaf N and photosynthetic capacity profiles are distributed along vertical light gradients such that the marginal gain of nitrogen investment is identical among leaves. However, observed photosynthetic N gradients in trees do not follow this prediction, and the causes for this apparent discrepancy remain uncertain. Our objective was to evaluate how hydraulic limitations potentially modify crown-level optimization in Sequoiadendron giganteum (giant sequoia) trees up to 90 m tall. Leaf water potential (ψ₁) and branch sap flow closely followed diurnal patterns of solar radiation throughout each tree crown. Minimum leaf water potential correlated negatively with height above ground, while leaf mass per area (LMA), shoot mass per area (SMA), leaf nitrogen content (% N), and bulk leaf stable carbon isotope ratios (δ¹³C) correlated positively with height. We found no significant vertical trends in maximum leaf photosynthesis (A), stomatal conductance (g s), and intrinsic water-use efficiency (A/g s), nor in branchaveraged transpiration (E L), stomatal conductance (G S), and hydraulic conductance (K L). Adjustments in hydraulic architecture appear to partially compensate for increasing hydraulic limitations with height in giant sequoia, allowing them to sustain global maximum summer water use rates exceeding 2000 kg day⁻¹. However, we found that leaf N and photosynthetic capacity do not follow the vertical light gradient, supporting the hypothesis that increasing limitations on water transport capacity with height modify photosynthetic optimization in tall trees.

Background Whether a modestly elevated homocysteine level is causally associated with an increased risk of cardiovascular disease remains unestablished. We conducted a Mendelian randomization study to assess the associations of circulating total homocysteine (tHcy) and B vitamin levels with cardiovascular diseases in the general population. Methods Independent single nucleotide polymorphisms associated with tHcy ( n  = 14), folate ( n  = 2), vitamin B6 ( n  = 1), and vitamin B12 ( n  = 14) at the genome-wide significance level were selected as instrumental variables. Summary-level data for 12 cardiovascular endpoints were obtained from genetic consortia, the UK Biobank study, and the FinnGen consortium. Results Higher genetically predicted circulating tHcy levels were associated with an increased risk of stroke. For each one standard deviation (SD) increase in genetically predicted tHcy levels, the odds ratio (OR) was 1.11 (95% confidence interval (CI), 1.03, 1.21; p  = 0.008) for any stroke, 1.26 (95% CI, 1.05, 1.51; p  = 0.013) for subarachnoid hemorrhage, and 1.11 (95% CI, 1.03, 1.21; p  = 0.011) for ischemic stroke. Higher genetically predicted folate levels were associated with decreased risk of coronary artery disease (OR SD , 0.88; 95% CI, 0.78, 1.00, p  = 0.049) and any stroke (OR SD , 0.86; 95% CI, 0.76, 0.97, p  = 0.012). Genetically predicted increased vitamin B6 levels were associated with a reduced risk of ischemic stroke (OR SD , 0.88; 95% CI, 0.81, 0.97, p  = 0.009). None of these associations persisted after multiple testing correction. There was no association between genetically predicted vitamin B12 and cardiovascular disease. Conclusions This study reveals suggestive evidence that B vitamin therapy and lowering of tHcy may reduce the risk of stroke, particularly subarachnoid hemorrhage and ischemic stroke.