Mendelian randomization is a causal inference method using genetic variations as instrumental variables, which skillfully takes advantages of the distributive randomness and timing priority of genetic variation, effectively avoiding confounding biases and reverse causalities in traditional observational researches. It has become a research hotspot in recent years. As a complex inflammatory disease, periodontitis is associated with many factors, but the cognitions about these associations are mostly based on traditional observational studies, lacking strong evidences to infer the causality. In order to bring up new research ideas in the periodontal field, this article mainly reviewed Mendelian randomization and its research progress in periodontitis.
Humans ; Mendelian Randomization Analysis ; Causality ; Periodontitis ; Research Design ; Genetic Variation

The condition of critically ill patients changes rapidly, involving pathological changes in multiple systems and organs throughout the body. Exploring the causal relationship of mechanisms can further reveal etiology, treatment, and prognosis of diseases. However, traditional prospective studies in the field of critical care are still subject to numerous limitations. As an emerging research method, Mendelian randomization (MR) analysis uses genetic variation to provide causal evidence for instrumental variables, which is expected to provide clues in critical diseases. This article systematically describes the research progresson the application of MR analysis in critical care medicine from four aspects: the principle of MR analysis, the difference between MR analysis and randomized controlled trial (RCT), the use of MR analysis in the field of critical illness, and the possible methods of application, aiming to provide possible directions for the research in this field.
Humans ; Mendelian Randomization Analysis/methods* ; Genetic Variation ; Causality ; Research Design

BACKGROUND: Thyroid dysfunction is associated with cardiovascular diseases. However, the role of thyroid function in lipid metabolism remains partly unknown. The present study aimed to investigate the causal association between thyroid function and serum lipid metabolism via a genetic analysis termed Mendelian randomization (MR). METHODS: The MR approach uses a genetic variant as the instrumental variable in epidemiological studies to mimic a randomized controlled trial. A two-sample MR was performed to assess the causal association, using summary statistics from the Atrial Fibrillation Genetics Consortium (n = 537,409) and the Global Lipids Genetics Consortium (n = 188,577). The clinical measures of thyroid function include thyrotropin (TSH), free triiodothyronine (FT3) and free thyroxine (FT4) levels, FT3:FT4 ratio and concentration of thyroid peroxidase antibodies (TPOAb). The serum lipid metabolism traits include total cholesterol (TC) and triglycerides, high-density lipoprotein, and low-density lipoprotein (LDL) levels. The MR estimate and MR inverse variance-weighted method were used to assess the association between thyroid function and serum lipid metabolism. RESULTS: The results demonstrated that increased TSH levels were significantly associated with higher TC (β = 0.052, P = 0.002) and LDL (β = 0.041, P = 0.018) levels. In addition, the FT3:FT4 ratio was significantly associated with TC (β = 0.240, P = 0.033) and LDL (β = 0.025, P = 0.027) levels. However, no significant differences were observed between genetically predicted FT4 and TPOAb and serum lipids. CONCLUSION Taken together, the results of the present study suggest an association between thyroid function and serum lipid metabolism, highlighting the importance of the pituitary-thyroid-cardiac axis in dyslipidemia susceptibility.
Lipid Metabolism/genetics* ; Mendelian Randomization Analysis ; Thyroid Function Tests ; Thyroid Gland ; Thyrotropin ; Thyroxine ; Triiodothyronine

Cardiovascular disease (CVD) is considered a primary driver of global mortality and is estimated to be responsible for approximately 17.9 million deaths annually. Consequently, a substantial body of research related to CVD has developed, with an emphasis on identifying strategies for the prevention and effective treatment of CVD. In this review, we critically examine the existing CVD literature, and specifically highlight the contribution of Mendelian randomization analyses in CVD research. Throughout this review, we assess the extent to which research findings agree across a range of studies of differing design within a triangulation framework. If differing study designs are subject to non-overlapping sources of bias, consistent findings limit the extent to which results are merely an artefact of study design. Consequently, broad agreement across differing studies can be viewed as providing more robust causal evidence in contrast to limiting the scope of the review to a single specific study design. Utilising the triangulation approach, we highlight emerging patterns in research findings, and explore the potential of identified risk factors as targets for precision medicine and novel interventions.
Artifacts ; Bias (Epidemiology) ; Cardiovascular Diseases ; Mendelian Randomization Analysis ; Mortality ; Precision Medicine ; Random Allocation ; Risk Factors

Mendelian Randomization Analysis ; Bone Density ; Genome-Wide Association Study ; Minerals ; Life Expectancy ; Polymorphism, Single Nucleotide

Humans ; Stroke, Lacunar ; Mendelian Randomization Analysis ; Leukocytes ; Telomere/genetics* ; Genome-Wide Association Study ; Polymorphism, Single Nucleotide

Pregnancy ; Humans ; Female ; Cesarean Section ; Mendelian Randomization Analysis ; Immune System Diseases

OBJECTIVE: To investigate the causal relationship between neutrophil extracellular trap (NET) and sepsis based on Mendelian randomization analysis. METHODS: The genome wide association study (GWAS) dataset for the NET biomarker myeloperoxidase (MPO)-DNA complex based on Donkel et al. 's Rotterdam study (RS) and GWAS dataset for identifying sepsis from the UK biobank were selected to screen single nucleotide polymorphisms (SNPS) associated with MPO-DNA complex as instrumental variable (IV) for genetic variation, using MPO-DNA complex as exposure factor. Potential causal associations between MPO-DNA complex and the risk of occurrence of sepsis, 28-day death from sepsis, need for intensive care due to sepsis, and 28-day death from sepsis requiring intensive care were analyzed using a two-sample, one-way Mendelian randomization analysis primary analysis method of inverse analysis of variance (IVW). Potential pleiotropy was assessed using the MR Egger regression intercept test. Sensitivity analysis was performed using the "leave one out" test. RESULTS: The GWAS data were obtained from a European population of both sexes, and the screening criteria was based on the three main assumptions of Mendelian randomization, resulting in 22 SNP entering the Mendelian randomization analysis. The results of the Mendelian randomization causal association effect analysis using the IVW method showed that for every standard deviation increase in the level of the MPO-DNA complex, the risk of sepsis increased by approximately 18% [odds ratio (OR) = 1.18, 95% confidence interval (95%CI) was 1.07-1.29, P < 0.001], the risk of 28-day death from sepsis increased by approximately 51% (OR = 1.51, 95%CI was 1.27-1.81, P < 0.001), an increase of approximately 38% in the risk of occurrence of needing intensive care due to sepsis (OR = 1.38, 95%CI was 1.12-1.70, P = 0.002), and an increase of approximately 125% in the risk of 28-day death from sepsis requiring intensive care (OR = 2.25, 95%CI was 1.21-4.18, P = 0.01). MR Egger regression intercept test suggested that there was no horizontal pleiotropy in the included SNP, and the MR-PRESSO test did not find outliers. Sensitivity analysis suggested that the results of Mendelian randomization were robust. CONCLUSIONS Rising NET can increase the risk of sepsis onset, progression and death as derived from Mendelian randomization analysis.
Female ; Male ; Humans ; Extracellular Traps ; Genome-Wide Association Study ; Mendelian Randomization Analysis ; Sepsis/genetics* ; Nonoxynol ; DNA

Mendelian randomization (MR) in epidemiology is the use of genetic variants as instrumental variables (IVs) in non-experimental design to make causality of a modifiable exposure on an outcome or disease. It assesses the causal effect between risk factor and a clinical outcome. The main reason to approach MR is to avoid the problem of residual confounding. There is no association between the genotype of early pregnancy and the disease, and the genotype of an individual cannot be changed. For this reason, it results with randomly assigned case-control studies can be set by regressing the measurements. IVs in MR are used genetic variants for estimating the causality. Usually an outcome is a disease and an exposure is risk factor, intermediate phenotype which may be a biomarker. The choice of the genetic variable as IV (Z) is essential to a successful in MR analysis. MR is named ‘Mendelian deconfounding’ as it gives to estimate of the causality free from biases due to confounding (C). To estimate unbiased estimation of the causality of the exposure (X) on the clinically relevant outcome (Y), Z has the 3 core assumptions (A1-A3). A1) Z is independent of C; A2) Z is associated with X; and A3) Z is independent of Y given X and C; The purpose of this review provides an overview of the MR analysis and is to explain that using an IV is proposed as an alternative statistical method to estimate causal effect of exposure and outcome under controlling for a confounder.
Bias (Epidemiology) ; Case-Control Studies ; Epidemiology ; Genotype ; Mendelian Randomization Analysis ; Methods ; Molecular Epidemiology ; Phenotype ; Pregnancy ; Random Allocation ; Risk Factors

OBJECTIVE: This study aimed to examine whether rheumatoid arthritis (RA) is causally associated with type 2 diabetes (T2D). METHODS: We performed a two-sample Mendelian randomization (MR) analysis using the inverse-variance weighted (IVW), weighted median, and MR-Egger regression methods. We used the publicly available summary statistics datasets from a genome-wide association studies (GWAS) meta-analysis of 5,539 autoantibody-positive individuals with RA and 20,169 controls of European descent, and a GWAS dataset of 10,247 individuals with T2D and 53,924 controls, overwhelmingly of European descent as outcomes. RESULTS: We selected 10 single-nucleotide polymorphisms from GWAS data on RA as instrumental variables to improve the inference. The IVW method supported a causal association between RA and T2D (β=0.044, standard error [SE]=0.022, p=0.047). The MR-Egger analysis showed a causal association between RA and T2D (β=0.093, SE=0.033, p=0.023). In addition, the weighted median approach supported a causal association between RA and T2D (β=0.056, SE=0.025, p=0.028). The association between RA and T2D was consistently observed using IVW, MR Egger, and weighted median methods. Cochran's Q test indicated no evidence of heterogeneity between instrumental variable estimates based on individual variants and MR-Egger regression revealed that directional pleiotropy was unlikely to have biased the results (intercept=−0.030; p=0.101). CONCLUSION: MR analysis supports that RA may be causally associated with an increased risk of T2D.
Arthritis, Rheumatoid ; Bias (Epidemiology) ; Dataset ; Genome-Wide Association Study ; Mendelian Randomization Analysis ; Methods ; Population Characteristics ; Random Allocation