To prevent chronic disease from getting worse, it is important to detect and predict it at an early stage. Therefore, the accuracy of the prediction is particularly important. To investigate the accuracy of different methods, this study compares the out-of-sample errors of machine learning algorithms and traditional econometric methods in predicting diabetes. The object of prediction in this study is fasting blood glucose, and the machine learning algorithms used are stepwise selection, bagging, random forests and support vector machine (SVM). In addition, we demonstrate the linear combination of above machine learning algorithms in this study. The findings indicate that the combined model outperforms both traditional econometric models and individual machine learning algorithms. However, the predictive performance of individual machine learning models does not consistently surpass that of traditional econometric approaches. Based on the data characteristics analyzed in this study, a possible explanation for this finding is that traditional econometric methods may exhibit superior performance in linear data prediction. Finally, the analysis of variable importance suggests that medical indicators and physical condition may play a more significant role in determining fasting blood glucose compared to hereditary factors. To further validate our results, we applied the same methodology to predict hypertension using the same dataset. The findings similarly indicated that the predictive ability of individual machine learning algorithms does not always surpass that of traditional econometric models. And a linear combination of the four machine learning algorithms enhances the predictive accuracy for hypertension.

Anhedonia is a core symptom of depression. Most anhedonia assessments rely on self-reporting, which does not accurately reflect hedonic capacity and is biased by individual subjectivity. Therefore, objective indicators are needed. Anhedonia may result in different speech features among depressive patients. Thus, speech features have become an emerging objective indicator in depression assessment. This study aims to investigate the relationship between anhedonia and speech features in individuals with depression by comparing the speech features of patients with and without anhedonia in a multitasking paradigm.

A total of 166 patients with depression were recruited for the study. Voice data were collected through the Verbal Fluency Test, Word reading, Video description, and Semi-structured Interviews. The primary analysis was performed using analysis of covariance (ANCOVA) and partial correlation analysis. We grouped patients based on the severity of depression and performed post-hoc t-tests or Mann-Whitney U tests for comparisons. The Benjamini-Hochberg method was used to control for False Discovery Rate in multiple comparisons.

After adjustment for anxiety severity (as measured by the 7-item Generalized Anxiety Disorder Scale, GAD-7), no significant differences in speech features were observed between patients with or without anhedonia. Similarly, after controlling for depression severity (as measured by the 17-item Hamilton Depression Scale, HAMD-17), no significant correlation was found between speech features and the degree of anhedonia. Post hoc analyses showed that seventeen speech features were correlated with depression severity (|r| < 0.3, small effect sizes), but no differences in speech features were found between patients with anhedonia and those without anhedonia within each subgroup based on depression severity.

Speech features do not differ significantly between patients with or without anhedonia at any level of depression severity. However, speech features were independently correlated with depression severity. Future studies may refine research methodology by optimizing speech task modules or assessing multidimensional prosodic features for more in-depth analysis.

Mendelian randomization (MR) is a technique which uses genetic data to uncover causal relationships between variables. With the growing availability of large-scale biobank data, there is increasing interest in elucidating nuances in these relationships using MR. Stratified MR techniques such as doubly-ranked MR (DRMR) and residual stratification MR have been developed to identify nonlinearity in causal relationships. These methods calculate causal estimates within strata of the exposure adjusted to mitigate the impact of collider bias. However, their application to scenarios using a stratifying variable other than the exposure to identify the presence of effect modifiers has been limited. The reliable identification of effect modifiers is key to identifying subgroups of patients differentially affected by risk and protective factors. In this study, we present a stratified MR algorithm capable of identifying effect modifiers of causal relationships using adapted forms of DRMR and residual stratification MR. Through simulations, the algorithm was found to be robust at handling nonlinear relationships and forms of collider bias, accommodating both binary and continuous outcomes. Application of the stratified MR algorithm to 1,715 exposure-stratifying variable-outcome combinations identified two Bonferroni significant effect modifiers of causal relationships in the UK Biobank. The causal effect of body mass index on type 2 diabetes mellitus was attenuated with age, while the effect of LDL cholesterol on coronary artery disease was exacerbated with increased serum urate. Overall, we introduce a tool for detecting effect modifiers of causal relationships, and present two cases with clinical implications for personalized risk assessment of cardiometabolic diseases.

Sarcoidosis is a complex inflammatory disease with a strong genetic component. Here, we perform a genome-wide association study in 9755 sarcoidosis cases to identify risk loci and map associated genes. We then use transcriptome-wide association studies and enrichment analyses to explore pathways involved in sarcoidosis and use Mendelian randomization to examine associations with modifiable factors and circulating biomarkers. We identify 28 genomic loci associated with sarcoidosis, with the C1orf141-IL23R locus showing the largest effect size. We observe gene expression patterns related to sarcoidosis in the spleen, whole blood, and lung, and highlight 75 tissue-specific genes through transcriptome-wide association studies. Furthermore, we use enrichment analysis to establish key roles for T cell activation, leukocyte adhesion, and cytokine production in sarcoidosis. Additionally, we find associations between sarcoidosis and genetically predicted body mass index, interleukin-23 receptor, and eight circulating proteins.

Previous studies suggest that smoking and higher alcohol consumption are associated with greater type 2 diabetes (T2D) risk. However, studies examining whether this reflects causal relationships are limited and often do not consider continuous glycaemic traits. We conducted both two-sample and one-sample Mendelian randomisation (MR), using publicly available GWAS data and UK Biobank data, respectively, to examine the potential causal effects of lifetime smoking index (LSI) and alcoholic drinks per week (DPW) on T2D and continuous traits (fasting glucose, fasting insulin and glycated haemoglobin, HbA1c). Two-sample MR results suggested possible causal effects of higher LSI on T2D risk (OR per 1SD higher LSI: 1.42, 95% CI 1.22 to 1.64); however, sensitivity analyses did not consistently support this finding. There was no robust evidence that higher DPW influenced T2D risk (OR per 1 SD higher log-transformed DPW: 1.04, 95% CI 0.40 to 2.65). There was evidence of a potential causal effect on higher fasting glucose (difference in mean fasting glucose in mmol/l per 1SD higher log-transformed DPW: 0.34, 95% CI 0.09 to 0.59), though, this was attenuated when accounting for body mass index (BMI), suggesting BMI confounding might explain the potential effect. One-sample MR results suggested a possible causal effect of higher DPW on T2D risk (OR per 1 SD higher log-transformed DPW: 1.71, 95% CI 1.24 to 2.36), but lower HbA1c levels (difference in mean SD of log transformed HbA1c (mmol/mol) per 1 SD higher log-transformed DPW: -0.07, 95% CI -0.11 to -0.02). Our results suggest effective public health interventions to prevent and/or reduce smoking and alcohol consumption are unlikely to reduce T2D prevalence.

We performed ancestry and sex specific Phenome Wide Association Studies (PheWAS) to explore disease related outcomes associated with genetically predicted height. This is the largest PheWAS on genetically predicted height involving up to 840,000 individuals of diverse ancestry. We explored European, African, East Asian ancestries and Hispanic population groups. Increased genetically predicted height is associated with hyperpotassemia and autism in the male cross-ancestry analysis. We report male-only European ancestry associations with anxiety disorders, post-traumatic stress and substance addiction and disorders. We identify a signal with benign neoplasm of other parts of digestive system in females. We report associations with a series of disorders, several with no prior evidence of association with height, involving mental disorders and the endocrine system. Our study suggests that increased genetically predicted height is associated with higher prevalence of many clinically relevant traits which has important implications for epidemiological and clinical disease surveillance and risk stratification.

Diabetic peripheral neuropathy (DPN) is the most common complication of diabetes mellitus. Early identification of individuals at high risk of DPN is essential for successful early intervention. Traditional Chinese medicine (TCM) tongue diagnosis, one of the four diagnostic methods, lacks specific algorithms for TCM symptoms and tongue features. This study aims to develop machine learning (ML) models based on TCM to predict the risk of diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes mellitus (T2DM).

A total of 4723 patients were included in the analysis (4430 with T2DM and 293 with DPN). TFDA-1 was used to obtain tongue images during a questionnaire survey. LASSO (least absolute shrinkage and selection operator) logistic regression model with fivefold cross-validation was used to select imaging features, which were then screened using best subset selection. The synthetic minority oversampling technique (SMOTE) algorithm was applied to address the class imbalance and eliminate possible bias. The area under the receiver operating characteristic curve (AUC) was used to evaluate the model's performance. Four ML algorithms, namely logistic regression (LR), random forest (RF), support vector classifier (SVC), and light gradient boosting machine (LGBM), were used to build predictive models for DPN. The importance of covariates in DPN was ranked using classifiers with better performance.

The RF model performed the best, with an accuracy of 0.767, precision of 0.718, recall of 0.874, F-1 score of 0.789, and AUC of 0.77. With a value of 0.879, the LGBM model appeared to be the best regarding recall Age, sweating, dark red tongue, insomnia, and smoking were the five most significant RF features. Age, yellow coating, loose teeth, smoking, and insomnia were the five most significant features of the LGBM model.

This cross-sectional study demonstrates that the RF and LGBM models can screen for high-risk DPN in T2DM patients using TCM symptoms and tongue features. The identified key TCM-related features, such as age, tongue coating, and other symptoms, may be advantageous in developing preventative measures for T2DM patients.

Overweight and obesity are a global pandemic, contributing to death and disability-adjusted life-years. Obesity is a major factor in the onset of chronic inflammatory diseases (CIDs). Yet, several knowledge gaps remain: For several CIDs, inconsistent results have been reported, relating to their obesity-imposed risk, data on most rare CIDs remain unavailable, sex differences and racial disparities remain mostly unaddressed.

A large-scale cohort study compared the risk of developing 46 CIDs in individuals with overweight/obesity (n=3,101,824) to an equal number of non-overweight/obese individuals. Propensity score matching optimized between-group comparability, and sensitivity analyses assessed study robustness.

The risk of developing any CID was 28.48% in overweight/obese individuals versus 17.55% in non-overweight/obese controls, with a hazard ratio (95%-confidence interval) of 1.52 (1.509-1.521, p<0.0001). This risk was consistent across all sensitivity, sex-, and race-stratified analyses. Overweight and obesity were associated with an increased risk for 24 of 46 CIDs in the primary analysis and all sensitivity analyses. For 12 diseases, increased risks were confirmed to one of the two sensitivity analyses, while for 10 diseases, results were discordant. No increased risk was observed for one disease. In sex-stratified analysis, overweight and obesity posed a more pronounced risk for four CIDs in female individuals. In race-stratified analysis, overweight and obesity were linked to a higher risk for seven CIDs in White individuals and to one CID in "Black or African American" individuals.

Overweight and obesity increase the risk for the majority of CIDs in a sex- and race-specific manner.

The molecular mechanisms underlying the association between type 2 diabetes (T2D) and gastrointestinal (GI) disease are unclear. To identify protein pathways, we conducted a two-stage network Mendelian randomisation (MR) study.

Genetic instruments for T2D were obtained from a large-scale summary-level genome-wide meta-analysis. Genetic associations with blood protein levels were obtained from three genome-wide association studies on plasma proteins (i.e. the deCODE study as the discovery and the UKB-PPP and Fenland studies as the replication). Summary-level data on 10 GI diseases were derived from genome-wide meta-analysis of the UK Biobank and FinnGen. MR and colocalisation analyses were performed. Pathways were constructed according to the directionality of total and indirect effects, and corresponding proportional mediation was estimated. Druggability assessments were conducted across four databases to prioritise protein mediators.

Genetic liability to T2D was associated with 69 proteins in the discovery protein dataset after multiple testing corrections. All associations were replicated at the nominal significance level. Among T2D-associated proteins, genetically predicted levels of nine proteins were associated with at least one of the GI diseases. Genetically predicted levels of SULT2A1 (odds ratio = 1.98, 95% CI 1.80-2.18), and ADH1B (odds ratio = 2.05, 95% CI 1.43-2.94) were associated with cholelithiasis and cirrhosis respectively. SULT2A1 and cholelithiasis (PH4 = 0.996) and ADH1B and cirrhosis (PH4 = 0.931) have strong colocalisation support, accounting for the mediation proportion of 72.8% (95% CI 45.7-99.9) and 42.9% (95% CI 15.5-70.4) respectively.

The study identified some proteins mediating T2D-GI disease associations, which provided biological insights into the underlying pathways.

The DNA methylation-based epigenetic clocks are increasingly recognized for their precision in predicting aging and its health implications. Although prior research has identified connections between accelerated epigenetic aging and multiple sclerosis, the chronological and causative aspects of these relationships are yet to be elucidated. Our research seeks to clarify these potential causal links through a bidirectional Mendelian randomization study.

This analysis employed statistics approaches from genome-wide association studies related to various epigenetic clocks (GrimAge, HannumAge, PhenoAge, and HorvathAge) and multiple sclerosis, utilizing robust instrumental variables from the Edinburgh DataShare (n = 34,710) and the International Multiple Sclerosis Genetics Consortium (including 24,091 controls and 14,498 cases). We applied the inverse-variance weighted approach as our main method for Mendelian randomization, with additional sensitivity analyses to explore underlying heterogeneity and pleiotropy.

Using summary-based Mendelian randomization, we found that HannumAge was associated with multiple sclerosis (OR = 1.071, 95%CI:1.006-1.140, p = 0.033, by inverse-variance weighted). The results suggest that an increase in epigenetic age acceleration of HannumAge promotes the risk of multiple sclerosis. In reverse Mendelian randomization analysis, no evidence of a clear causal association of multiple sclerosis on epigenetic age acceleration was identified.

Our Mendelian randomization analysis revealed that epigenetic age acceleration of HannumAge was causally associated with multiple sclerosis, and provided novel insights for further mechanistic and clinical studies of epigenetic age acceleration-mediated multiple sclerosis.

The complicated neurological syndrome known as multiple sclerosis (MS) is typified by demyelination, inflammation, and neurodegeneration in the central nervous system (CNS). Managing this crippling illness requires an understanding of the complex interactions between neurophysiological systems, diagnostic techniques, and therapeutic methods. A complex series of processes, including immunological dysregulation, inflammation, and neurodegeneration, are involved in the pathogenesis of MS. Gene predisposition, autoreactive T cells, B cells, and cytokines are essential participants in the development of the disease. Demyelination interferes with the ability of the CNS to transmit signals, which can cause a variety of neurological symptoms, including impaired motor function, sensory deficiencies, and cognitive decline. Developing tailored therapeutics requires understanding the underlying processes guiding the course of the disease. Neuroimaging, laboratory testing, and clinical examination are all necessary for an accurate MS diagnosis. Evoked potentials and cerebrospinal fluid studies assist in verifying the diagnosis, but magnetic resonance imaging (MRI) is essential for identifying distinctive lesions in the CNS. Novel biomarkers have the potential to increase diagnostic precision and forecast prognosis. The goals of MS treatment options are to control symptoms, lower disease activity, and enhance quality of life. To stop relapses and reduce the course of the disease, disease-modifying treatments (DMTs) target several components of the immune response. DMTs that are now on the market include interferons, glatiramer acetate, monoclonal antibodies, and oral immunomodulators; each has a unique mode of action and safety profile. Symptomatic treatments improve patients' general well-being by addressing specific symptoms, including pain, sphincter disorders, fatigue, and spasticity. Novel treatment targets, neuroprotective tactics, and personalized medicine techniques will be the main focus of MS research in the future. Improving long-term outcomes for MS patients and optimizing disease treatment may be possible by utilizing immunology, genetics, and neuroimaging developments. This study concludes by highlighting the complexity of multiple MS, including its changing therapeutic landscape, diagnostic problems, and neurophysiological foundations. A thorough grasp of these elements is essential to improving our capacity to identify, manage, and eventually overcome this intricate neurological condition.

Mendelian randomization (MR), i.e. instrumental variable analysis using genetic instruments, is an approach that incorporates population genetics to improve causal inference. Given that genetics are randomly allocated at conception, this resembles the randomization process in randomized controlled trials and hence is more resistant to unobserved confounding compared to conventional observational studies (e.g. cohort studies). The seminar paper briefly described the origin of MR and its underlying assumptions (relevance, independence, and exclusion restriction). This was followed by introducing one sample MR designs (in which instrument-exposure and instrument-outcome associations are derived from the same sample) and one sample MR design (in which instrument-exposure and instrument-outcome associations are derived from different samples). The seminar paper then summarized key aspects of MR studies, such as instrument selection, data sources for conducting MR studies, and statistical analyses. Variations of MR design were also introduced, such as how this design can inform the effect of drug targets (drug target MR). The STROBE-MR checklist and relevant MR guidelines were introduced. The seminar paper concluded by discussing the credibility crisis of MR studies.

Currently, there is limited and inconsistent evidence regarding the causal relationship between systemic inflammatory regulators and two common types of neuropathic pain, namely, postherpetic neuralgia (PHN) and trigeminal neuralgia (TN). This study employed a Mendelian randomization (MR) approach to investigate the causal relationship between systemic inflammatory regulators and these two common neuropathic pain conditions.

In this study, 41 single-nucleotide polymorphisms (SNPs) associated with PHN and TN were selected as instrumental variables (IVs) representing systemic inflammatory regulators. Genetic associations of systemic inflammatory regulators were derived from recent genome-wide association studies (GWAS) on the human proteome and cytokines. Genetic data related to PHN and TN were obtained from the FinnGen. The primary analytical method utilized inverse variance weighting (IVW) and various sensitivity analyses.

Prior to applying the false discovery rate (FDR) correction, our bidirectional MR analysis revealed that increased levels of IFNγ (OR: 0.46, 95% CI: 0.24-0.87, PIVW: 0.016) and MCP3 (OR: 0.52, 95% CI: 0.35-0.77, PIVW: 0.001) were associated with a reduced risk of PHN, and increased levels of IL-16 (OR: 0.81, 95% CI: 0.67-0.98, PIVW: 0.026) were causally associated with a reduced risk of TN. In discussing the impact of PHN and TN on systemic inflammatory regulator levels, we observed the following findings: The BETA for CTACK was -0.07 (95% CI: -0.13 to -0.01, PIVW: 0.015), the BETA for FGFBasic was -0.04 (95% CI: -0.08 to -0.01, PIVW: 0.020), and the BETA for IL-17 was -0.04 (95% CI: -0.08 to -0.01, PIVW: 0.019). These results indicate that patients with PHN tend to have lower levels of CTACK, FGFBasic, and IL-17. Conversely, the BETA for IFNγ was -0.09 (95% CI: -0.18 to 0.00, PIVW: 0.046), suggesting that patients with TN tend to have lower levels of IFN γ. However, after FDR correction, only the association between MCP3 and PHN remained statistically significant (PFDR: 0.044).

This study found that certain systemic inflammatory regulators are associated with PHN and TN to some extent. However, further research is needed to explore the specific mechanisms underlying these connections.

Mendelian randomization is an epidemiologic technique that can explore the potential effect of perturbing a pharmacological target. Plasma caffeine levels can be used as a biomarker to measure the pharmacological effects of caffeine. Alternatively, this can be assessed using a behavioral proxy, such as average number of caffeinated drinks consumed per day. Either variable can be used as the exposure in a Mendelian randomization investigation, and to select which genetic variants to use as instrumental variables. Another possibility is to choose variants in gene regions with known biological relevance to caffeine level regulation. These choices affect the causal question that is being addressed by the analysis, and the validity of the analysis assumptions. Further, even when using the same genetic variants, the sign of Mendelian randomization estimates (positive or negative) can change depending on the choice of exposure. Some genetic variants that decrease caffeine metabolism associate with higher levels of plasma caffeine, but lower levels of caffeine consumption, as individuals with these variants require less caffeine consumption for the same physiological effect. We explore Mendelian randomization estimates for the effect of caffeine on body mass index, and discuss implications for variant and exposure choice in drug target Mendelian randomization investigations.

FADS1 rs174556 polymorphism influences on dietary fats metabolism and type 2 diabetes (T2DM). This study aimed to compare the effect of three oils of sesame, canola and sesame-canola on cardio metabolic factors across genotypes of rs174556 variant in patients with type 2 of diabetes. This study was a randomized triple-blind three-way cross-over clinical trial. 95 Subjects with T2DM replaced their regular dietary oil with sesame oil, canola oil, or sesame-canola oil for three 9-week phases and completed the study. There were three anthropometric measurements, blood sampling and biochemical assessments at the beginning, middle, and at the end of each phase for assessments. Genotyping was conducted using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. In the crude model, there was an interaction between consumed oils and rs174556 variant on serum concentration of Apolipoprotein A-I (ApoA-1). During intake of sesame oil, lower levels of triglycerides (TG) were observed in individuals with TT genotype compared to C allele carriers' allele, which remained significant in adjusted models. Compared to C allele carrier's, the people with TT genotype experienced significant increase and decrease in serum levels of HDL and TG, respectively in adjusted models. Also, the subjects who consumed sesame-canola oil had lower serum concentrations of fasting blood glucose than those who received sesame and canola oils, regardless of used oils and genotypes. FADS1 Gene variant (rs174556) might modify cardiometabolic changes following dietary vegetable oils. Larger longitudinal studies especially randomized clinical trials are needed to clarify these associations.

We aimed to assess temporal trends in type 2 diabetes mellitus (T2DM)-related deaths and disability-adjusted life years (DALYs) at global and cross-social demographic index (SDI) levels, using data from the Global Burden of Disease (GBD) in 2021.

We used geospatial mapping to visualize the global distribution of T2DM-related mortality and DALYs in 2021. Joinpoint regression assessed annual and average percent changes in DALYs and deaths from 1990 to 2021 across SDI regions. Age-period-cohort modeling examined the effects of age, period, and cohort on trends. Decomposition analysis evaluated the impact of population growth, aging, and epidemiological changes on DALY trends. A stratified projection forecasted future T2DM burden by age and sex from 2020 to 2044.

T2DM-related mortality and DALYs were highest in low-SDI regions. Globally, T2DM-related deaths and DALYs have increased, with the most rapid rise in low and low-middle SDI regions, driven by population growth and epidemiological shifts. High-SDI countries showed a slower increase in DALYs, influenced more by aging. Age-period-cohort analysis indicated higher DALY rates in later birth cohorts and recent periods, especially in high-SDI regions. Future projections show a significant increase in the 70-74 age group and a gradual rise in other age groups.

The burden of T2DM is projected to continue increasing, especially in low-SDI and low-middle SDI regions, where population growth and epidemiological shifts are the main contributors. This underscores the need for targeted, region-specific healthcare policies, preventive strategies, and age-specific interventions to address the increasing T2DM burden globally.

Prediabetes and type 2 diabetes mellitus are growing global health concerns, predisposing individuals to various vascular complications. Lifestyle modifications, including dietary interventions, offer promising avenues for prevention and management. Using a multivariable-adjusted model, we analyzed the cross-sectional associations between plasma proportions (% of total fatty acids) of omega-3 polyunsaturated fatty acids (n3 PUFA, including total n3 PUFA, docosahexaenoic acid [DHA], non-DHA n3 PUFA), and glycated hemoglobin A1c (HbA1c) as well as the prevalence of prediabetes in a sample from the UK Biobank cohort. Our hypothesis was that proportions of n3 PUFA, especially DHA, would by inversely associated with the prediabetes prevalence. The sample (n = 92,762; 54.5% females) had an average age of 56 years and was overweight (mean body mass index = 27). The mean plasma DHA proportion in the sample was 2.03% (standard deviation [SD] = 0.67%), non-DHA n3 PUFA was 2.41% (SD = 1.02%) and total n3 PUFA was 4.43% (SD = 1.56%). Prediabetic individuals were identified by blood HbA1c proportions between 5.7% and 6.4% (39-46 mmol/mol) according to American Diabetes Association criteria. Each of the three n3 PUFA biomarkers was inversely associated with HbA1c proportions. In particular, DHA showed the strongest inverse association, with an OR of 0.62 (95% confidence intervals: 0.58, 0.67; P < .001) when comparing quintiles 5 to 1 in a fully adjusted model. These findings suggest a potential protective role of n3 PUFA, particularly DHA, in mitigating the risk of having prediabetes. Further prospective investigations are needed to clarify whether long-chain n3 PUFA could function as modifiable factors for prediabetes.

Observational studies revealed paradoxically inverse associations between diabetes and aortic diseases (aortic aneurysm or aortic dissection), yet the causality remains to be determined. To investigate the causal associations between diabetes and aortic diseases using Mendelian randomization (MR) analyses. Summary-level data for exposures (type 1 diabetes, type 2 diabetes, fasting glucose, fasting insulin, glycated hemoglobin) and outcomes (aortic dissection and aortic aneurysm) were obtained from public genome-wide association study data. The principal analysis was the inverse-variance weighted (IVW) method. Sensitivity analyses were also carried out, including weighted median, MR-Egger, and multivariable MR methods. According to IVW results, type 1 diabetes (odds ratio [OR]: 0.99; 95% confidence interval [CI] 0.93-1.07; P = 0.87), type 2 diabetes (OR: 0.97; 95% CI 0.77-1.20; P = 0.75), fasting glucose (OR: 1.16; 95% CI 0.48-2.84; P = 0.74), fasting insulin (OR: 2.75; 95% CI 0.53-14.26; P = 0.23), or glycated hemoglobin (OR: 0.33; 95% CI 0.09-1.17; P = 0.09) had no causal effect on aortic dissection. Similarly, type 1 diabetes, type 2 diabetes, fasting glucose, fasting insulin, or glycated hemoglobin had no causal effect on aortic aneurysm. Sensitivity analyses revealed consistent results. MR-Egger method and funnel plot yielded no indication of directional pleiotropy. Diabetes had no causal associations with aortic dissection or aortic aneurysm. The observed inverse associations in previous cohort studies may be explained by confounding factors or reverse causation.

Accurate predictive models of future disease onset are crucial for effective preventive healthcare, yet longitudinal data sets linking early risk factors to subsequent health outcomes are limited. To overcome this challenge, we introduce a novel framework, Predictive Risk modeling using Mendelian Randomization (PRiMeR), which utilizes genetic effects as supervisory signals to learn disease risk predictors without relying on longitudinal data. To do so, PRiMeR leverages risk factors and genetic data from a healthy cohort, along with results from genome-wide association studies of diseases of interest. After training, the learned predictor can be used to assess risk for new patients solely based on risk factors. We validate PRiMeR through comprehensive simulations and in future type 2 diabetes predictions in UK Biobank participants without diabetes, using follow-up onset labels for validation. Moreover, we apply PRiMeR to predict future Alzheimer's disease onset from brain imaging biomarkers and future Parkinson's disease onset from accelerometer-derived traits. Overall, with PRiMeR we offer a new perspective in predictive modeling, showing it is possible to learn risk predictors leveraging genetics rather than longitudinal data.

A genetic Gaussian network of multiple phenotypes, constructed through the inverse matrix of the genetic correlation matrix, is informative for understanding the biological dependencies of the phenotypes. However, its estimation may be challenging because the genetic correlation estimates are biased due to estimation errors and idiosyncratic pleiotropy inherent in GWAS summary statistics. Here, we introduce a novel approach called estimation of genetic graph (EGG), which eliminates the estimation error bias and idiosyncratic pleiotropy bias with the same techniques used in multivariable Mendelian randomization. The genetic network estimated by EGG can be interpreted as shared common biological contributions between phenotypes, conditional on others. We use both simulations and real data to demonstrate the superior efficacy of our novel method in comparison with the traditional network estimators.

Two researchers independently assessed studies published up to February 5, 2023, across PubMed, Web of Science, Embase, and Cochrane Library, to investigate the associations of sleep traits with cardiometabolic risk factors, as well as with cardiovascular diseases. Fourteen systematic reviews consisting of 23 meta-analyses, and 11 Mendelian randomization (MR) studies were included in this study. Short sleep duration was associated with a higher risk of obesity, type 2 diabetes (T2D), hypertension, stroke, and coronary heart disease (CHD) in observational studies, while a causal role was only demonstrated in obesity, hypertension, and CHD by MR. Similarly, long sleep duration showed connections with a higher risk of obesity, T2D, hypertension, stroke, and CHD in observational studies, none was supported by MR analysis. Both observational and MR studies indicated heightened risks of hypertension, stroke, and CHD in relation to insomnia. Napping was linked to elevated risks of T2D and CHD in observational studies, with MR analysis confirming a causal role in T2D. Additionally, snoring was correlated with increased risks of stroke and CHD in both observational and MR studies. This work consolidates existing evidence on a causal relationship between sleep characteristics and cardiometabolic risk factors, as well as cardiovascular diseases.

Recent research extends our knowledge of plasma lipid species, building on established links between serum lipid levels and Type 2 Diabetes Mellitus (T2DM) risk. Identifying the causal roles of these lipid species is key to improving T2DM risk assessment.

This study employs Mendelian randomization (MR) to investigate the causal relationship between 179 lipid species across 13 lipid categories and T2DM. Summary-level data were sourced from genome-wide association studies. The primary analytical methods included the inverse variance weighted (IVW) approach and the Wald ratio, complemented by a series of sensitivity analyses to ensure the robustness of results. The IVW analysis reveals a significant causal association between elevated levels of ceramide (d40:2) (OR = 1.071, 95% CI 1.034-1.109, P = 1.36 × 10-4), sphingomyelin (d38:1) (OR = 1.052, 95% CI 1.028-1.077, P = 1.80 × 10-5), and triacylglycerol (56:8) (OR = 1.174, 95% CI 1.108-1.243, P = 4.65 × 10-8), and an increased risk of T2DM. Conversely, Wald ratio analysis indicates that higher levels of phosphatidylcholine (O-16:1_16:0) (OR = 0.928, 95% CI 0.892-0.966, P = 2.37 × 10-4), phosphatidylcholine (O-16:1_20:4) (OR = 0.932, 95% CI 0.897-0.967, P = 2.37 × 10-4), and phosphatidylcholine (O-18:2_20:4) (OR = 0.872, 95% CI 0.812-0.935, P = 1.24 × 10-4) are significantly associated with a reduced risk of T2DM. Furthermore, suggestive causal evidence for 22 additional lipid species was identified.

This MR study establishes a causal relationship between specific lipid classes in modulating the risk of T2DM. It offers new insights for risk assessment and potential therapeutic targets in T2DM.

Dyslipidemia is one of the cardiometabolic risk factors that influences mortality globally. Unraveling the causality between blood lipids and metabolites and the complex networks connecting lipids, metabolites, and other cardiometabolic traits can help to more accurately reflect the body's metabolic disorders and even cardiometabolic diseases. We conducted targeted metabolomics of 248 metabolites in 437 twins from the Chinese National Twin Registry. Inference about Causation through Examination of FAmiliaL CONfounding (ICE FALCON) analysis was used for causal inference between metabolites and lipid parameters. Bidirectional mediation analysis was performed to explore the linkages between blood lipids, metabolites, and other seven cardiometabolic traits. We identified 44, 1, and 31 metabolites associated with triglyceride (TG), total cholesterol (TC), and high-density lipoprotein-cholesterol (HDL-C), most of which were gut microbiota-derived metabolites. There were 9, 1, and 14 metabolites that showed novel associations with TG, TC, and HDL-C, respectively. ICE FALCON analysis found that TG and HDL-C may have a predicted causal effect on 23 and six metabolites, respectively, and one metabolite may have a predicted causal effect on TG. Mediation analysis discovered 14 linkages connecting blood lipids, metabolites, and other cardiometabolic traits. Our study highlights the significance of gut microbiota-derived metabolites in lipid metabolism. Most of the identified cross-sectional associations may be due to the lipids having a predicted causal effect on metabolites, but not vice versa, nor are they due to family confounding. These findings shed new light on lipid metabolism and personalized management of cardiometabolic diseases.

Extensive observational evidence has suggested an association between depression and type 2 diabetes (T2D). However, the causal relationships between these two diseases require further investigation. This study aimed to evaluate the bidirectional causal effect between two types of depression and T2D using two-sample Mendelian randomization (MR).

We applied two-step MR techniques, using single-nucleotide polymorphisms (SNPs) as the genetic instruments for analysis. We utilized summary data from genome-wide association studies (GWASs) for major depression (MD), depressive status (frequency of depressed mood in the last two weeks), T2D, and other known T2D risk factors such as obesity, sedentary behavior (time spent watching television), and blood pressure. The analysis utilized inverse variance weighted (IVW), MR-Egger regression, weighted median, weighted mode, MR pleiotropy residual sum, and outlier methods to determine potential causal relationships.

The study found that MD was positively associated with T2D, with an odds ratio (OR) of 1.26 (95% CI: 1.10-1.43, p = 5.6×10-4) using the IVW method and an OR of 1.21 (95% CI: 1.04-1.41, p = 0.01) using the weighted median method. Depressive status was also positively associated with T2D, with an OR of 2.26 (95% CI: 1.03-4.94, p = 0.04) and an OR of 3.62 (95% CI: 1.33-9.90, p = 0.01) using the IVW and weighted median methods, respectively. No causal effects of MD and depressive status on T2D risk factors were observed, and T2D did not influence these factors.

Our study demonstrates a causal relationship between depression and an increased risk of developing T2D, with both major depression and depressive status being positively associated with T2D.

The evidence for joint and independent associations of low muscle mass and low muscle strength with diabetes is limited and mixed. The study aimed to determine the associations of muscle parameters (muscle mass, strength, quality, and sarcopenia) and sarcopenia obesity with diabetes, and the previously unstudied mediating effect of inflammation.

A total of 13,420 adults from the 2023 China National Health Survey (CNHS) and 5,380 adults from the 2011-2014 National Health and Nutrition Examination Survey (NHANES) were included in this study. Muscle mass was determined using bioelectrical impedance analysis (BIA) in the CNHS, and whole-body dual X-ray absorptiometry (DXA) in the NHANES. Muscle strength was assessed using digital hand dynamometer. Multivariate logistic regression models were used to evaluate the associations of muscle parameters and sarcopenia obesity with diabetes. Inflammatory status was assessed using blood cell counts and two systemic inflammation indices (platelet-to-lymphocyte ratio (PLR) and system inflammation response index (SIRI)). Mediation analysis was conducted to examine inflammation's role in these associations.

Low muscle mass and strength were independently related to diabetes. Low muscle quality was associated with elevated diabetes risk. Sarcopenia has a stronger association with diabetes compared to low muscle strength alone or mass alone (CNHS, odds ratio (OR) = 1.93, 95 % confidence interval (CI):1.64-2.27; NHANES, OR = 3.80, 95 %CI:2.58-5.58). Participants with sarcopenia obesity exhibit a higher risk of diabetes than those with obesity or sarcopenia alone (CNHS, OR = 2.21, 95 %CI:1.72-2.84; NHANES, OR = 6.06, 95 %CI:3.64-10.08). Associations between muscle parameters and diabetes were partially mediated by inflammation (mediation proportion: 1.99 %-36.64 %, P < 0.05).

Low muscle mass and muscle strength are independently or jointly associated with diabetes, and inflammation might be a potential mechanism underlying this association. Furthermore, the synergistic effects of sarcopenia and obesity could significantly increase diabetes risk.

While the association between coeliac disease and type 1 diabetes is well documented, the association of coeliac disease with type 2 diabetes risk remains undetermined. We conducted a nationwide cohort and Mendelian randomisation analysis to investigate this link.

This nationwide matched cohort used data from the Swedish ESPRESSO cohort including 46,150 individuals with coeliac disease and 219,763 matched individuals in the comparator group selected from the general population, followed up from 1969 to 2021. Data from 9053 individuals with coeliac disease who underwent a second biopsy were used to examine the association between persistent villous atrophy and type 2 diabetes. Multivariable Cox regression was employed to estimate the associations. In Mendelian randomisation analysis, 37 independent genetic variants associated with clinically diagnosed coeliac disease at p<5×10-8 were used to proxy genetic liability to coeliac disease. Summary-level data for type 2 diabetes were obtained from the DIAGRAM consortium (80,154 cases) and the FinnGen study (42,593 cases).

Over a median 15.7 years' follow-up, there were 6132 (13.3%) and 30,138 (13.7%) incident cases of type 2 diabetes in people with coeliac disease and comparator individuals, respectively. Those with coeliac disease were not at increased risk of incident type 2 diabetes with an HR of 1.00 (95% CI 0.97, 1.03) compared with comparator individuals. Persistent villous atrophy was not associated with an increased risk of type 2 diabetes compared with mucosal healing among participants with coeliac disease (HR 1.02, 95% CI 0.90, 1.16). Genetic liability to coeliac disease was not associated with type 2 diabetes in DIAGRAM (OR 1.01, 95% CI 0.99, 1.03) or in FinnGen (OR 1.01, 95% CI 0.99-1.04).

Coeliac disease was not associated with type 2 diabetes risk.

Self-reported shorter/longer sleep duration, insomnia, and evening preference are associated with hyperglycaemia in observational analyses, with similar observations in small studies using accelerometer-derived sleep traits. Mendelian randomization (MR) studies support an effect of self-reported insomnia, but not others, on glycated haemoglobin (HbA1c). To explore potential effects, we used MR methods to assess effects of accelerometer-derived sleep traits (duration, mid-point least active 5-h, mid-point most active 10-h, sleep fragmentation, and efficiency) on HbA1c/glucose in European adults from the UK Biobank (UKB) (n = 73,797) and the MAGIC consortium (n = 146,806). Cross-trait linkage disequilibrium score regression was applied to determine genetic correlations across accelerometer-derived, self-reported sleep traits, and HbA1c/glucose. We found no causal effect of any accelerometer-derived sleep trait on HbA1c or glucose. Similar MR results for self-reported sleep traits in the UKB sub-sample with accelerometer-derived measures suggested our results were not explained by selection bias. Phenotypic and genetic correlation analyses suggested complex relationships between self-reported and accelerometer-derived traits indicating that they may reflect different types of exposure. These findings suggested accelerometer-derived sleep traits do not affect HbA1c. Accelerometer-derived measures of sleep duration and quality might not simply be 'objective' measures of self-reported sleep duration and insomnia, but rather captured different sleep characteristics.

The triglyceride glucose-body mass index (TyG-BMI) is a convenient and clinically significant indicator of insulin resistance. This study aims to investigate the correlation between TyG-BMI and the onset of new-onset diabetes and determine an optimal reflection point for TyG-BMI. An analysis was conducted on 1917 participants from the risk evaluation of cancers in Chinese diabetic individuals: a lONgitudinal (REACTION) study. Participants were categorized based on their TyG-BMI, and the relationship between TyG-BMI and the incidence of new-onset diabetes was explored through logistic regression models, smoothed curve fitting with restricted cubic spline, and a two-piecewise logistic regression model. The mean age of the participants was 57.60 ± 8.89 years, with 66.5% being females. The mean TyG-BMI was 223.3 ± 32.8. Ultimately, 137 individuals (7.1%) progressed to diabetes after three years. After adjusting for covariates, TyG-BMI exhibited a positive correlation with new-onset diabetes (odd ratios (OR) for each standard deviation increase = 1.330, 95% CI 1.110-1.595). The relationship between TyG-BMI and new-onset diabetes was non-linear, with a inflcetion point at 202.9. This study reveals a positive non-linear relationship between TyG-BMI and the risk of new-onset diabetes in Chinese middle-aged and elderly individuals. When TyG-BMI exceeds 202.9, there is a significantly heightened risk of new-onset diabetes. These findings offer valuable insights for preventing new-onset diabetes.

Simple and practical tools for screening high-risk new-onset diabetes after percutaneous coronary intervention (PCI) (NODAP) are urgently needed to improve post-PCI prognosis. We aimed to evaluate the risk factors for NODAP and develop an online prediction tool using conventional variables based on a multicenter database. China evidence-based Chinese medicine database consisted of 249, 987 patients from 4 hospitals in mainland China. Patients ≥ 18 years with implanted coronary stents for acute coronary syndromes and did not have diabetes before PCI were enrolled in this study. According to the occurrence of new-onset diabetes mellitus after PCI, the patients were divided into NODAP and Non-NODAP. After least absolute shrinkage and selection operator regression and logistic regression, the model features were selected and then the nomogram was developed and plotted. Model performance was evaluated by the receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test and decision curve analysis. The nomogram was also externally validated at a different hospital. Subsequently, we developed an online visualization tool and a corresponding risk stratification system to predict the risk of developing NODAP after PCI based on the model. A total of 2698 patients after PCI (1255 NODAP and 1443 non-NODAP) were included in the final analysis based on the multicenter database. Five predictors were identified after screening: fasting plasma glucose, low-density lipoprotein cholesterol, hypertension, family history of diabetes and use of diuretics. And then we developed a web-based nomogram ( https://mr.cscps.com.cn/wscoringtool/index.html ) incorporating the above conventional factors for predicting patients at high risk for NODAP. The nomogram showed good discrimination, calibration and clinical utility and could accurately stratify patients into different NODAP risks. We developed a simple and practical web-based nomogram based on multicenter database to screen for NODAP risk, which can assist clinicians in accurately identifying patients at high risk of NODAP and developing post-PCI management strategies to improved patient prognosis.

Are there associations of age at menarche (AAM) with health-related outcomes in East Asians?

AAM is associated with osteoporosis, Type 2 diabetes (T2D), glaucoma, and uterine fibroids, as demonstrated through observational studies, polygenic risk scores, genetic correlations, and Mendelian randomization (MR), with additional findings indicating a causal effect of BMI and T2D on earlier AAM.

Puberty timing is linked to adult disease risk, but research predominantly focuses on European populations, with limited studies in other groups.

We performed an AAM genome-wide association study (GWAS) with 57 890 Han Taiwanese females and examined the association between AAM and 154 disease outcomes using the Taiwanese database. Additionally, we examined genetic correlations between AAM and 113 diseases and 67 phenotypes using Japanese GWAS summary statistics.

We performed AAM GWAS and gene-based GWAS studies to obtain summary statistics and identify potential AAM-related genes. We applied phenotype, polygenic risk scores, and genetic correlation analyses of AAM to explore health-related outcomes, using multivariate regression and linkage disequilibrium score regression analyses. We also explored potential bidirectional causal relationships between AAM and related outcomes through univariable and multivariable MR analyses.

Fifteen lead single-nucleotide polymorphisms and 24 distinct genes were associated with AAM in Taiwan. AAM was genetically associated with later menarche and menopause, greater height, increased osteoporosis risk, but lower BMI, and reduced risks of T2D, glaucoma, and uterine fibroids in East Asians. Bidirectional MR analyses indicated that higher BMI/T2D causally leads to earlier AAM.

Our findings were specific to Han Taiwanese individuals, with genetic correlation analyses conducted in East Asians. Further research in other ethnic groups is necessary.

Our study provides insights into the genetic architecture of AAM and its health-related outcomes in East Asians, highlighting causal links between BMI/T2D and earlier AAM, which may suggest potential prevention strategies for early puberty.

The work was supported by China Medical University, Taiwan (CMU110-S-17, CMU110-S-24, CMU110-MF-49, CMU111-SR-158, CMU111-MF-105, CMU111-MF-21, CMU111-S-35, CMU112-SR-30, and CMU112-MF-101), the China Medical University Hospital, Taiwan (DMR-111-062, DMR-111-153, DMR-112-042, DMR-113-038, and DMR-113-103), and the Ministry of Science and Technology, Taiwan (MOST 111-2314-B-039-063-MY3, MOST 111-2314-B-039-064-MY3, MOST 111-2410-H-039-002-MY3, and NSTC 112-2813-C-039-036-B). The funders had no influence on the data collection, analyses, or conclusions of the study. No conflict of interests to declare.

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Inflammatory dermatoses such as atopic dermatitis (AD) have long been linked to the pathogenesis of diabetes mellitus. Indeed, numerous studies show an increased risk of diabetes mellitus in individuals with AD although lower prevalence of diabetes mellitus is also observed in few studies. Though the underlying mechanisms accounting for the reciprocal influence between these two conditions are still unclear, the complex interplay between diabetes mellitus and AD is attributable, in part, to genetic and environmental factors, cytokines, epidermal dysfunction, as well as drugs used for the treatment of AD. Proper management of one condition can mitigate the other condition. In this review, we summarize the evidence of the interaction between diabetes mellitus and AD, and discuss the possible underlying mechanisms by which these two conditions influence each other.

Chronic low-grade inflammatory profile (CLIP) is one of the pathways involved in type 2 diabetes (T2D). Currently, there is limited evidence for ameliorating effects of combined lifestyle interventions on CLIP in type 2 diabetes. We investigated whether a 13-week combined lifestyle intervention, using hypocaloric diet and resistance exercise plus high-intensity interval training with or without consumption of a protein drink, affected CLIP in older adults with T2D.

In this post-hoc analysis of the PROBE study 114 adults (≥55 years) with obesity and type 2 (pre-)diabetes had measurements of C-reactive protein (CRP), pro-inflammatory cytokines interleukin (IL)-6, tumor-necrosis-factor (TNF)-α, and monocyte chemoattractant protein (MCP)-1, anti-inflammatory cytokines IL-10, IL-1 receptor antagonist (RA), and soluble tumor-necrosis-factor receptor (sTNFR)1, adipokines leptin and adiponectin, and glycation biomarkers carboxymethyl-lysine (CML) and soluble receptor for advanced glycation end products (sRAGE) from fasting blood samples. A linear mixed model was used to evaluate change in inflammatory biomarkers after lifestyle intervention and effect of the protein drink. Linear regression analysis was performed with parameters of body composition (by dual-energy X-ray absorptiometry) and parameters of insulin resistance (by oral glucose tolerance test).

There were no significant differences in CLIP responses between the protein and the control groups. For all participants combined, IL-1RA, leptin and adiponectin decreased after 13 weeks (p = 0.002, p < 0.001 and p < 0.001), while ratios TNF-α/IL-10 and TNF-α/IL-1RA increased (p = 0.003 and p = 0.035). CRP increased by 12 % in participants with low to average CLIP (pre 1.91 ± 0.39 mg/L, post 2.13 ± 1.16 mg/L, p = 0.006) and decreased by 36 % in those with high CLIP (pre 5.14 mg/L ± 1.20, post 3.30 ± 2.29 mg/L, p < 0.001). Change in leptin and IL-1RA was positively associated with change in fat mass (β = 0.133, p < 0.001; β = 0.017, p < 0.001) and insulin resistance (β = 0.095, p = 0.024; β = 0.020, p = 0.001). Change in lean mass was not associated with any of the biomarkers.

13 weeks of combined lifestyle intervention, either with or without protein drink, reduced circulating adipokines and anti-inflammatory cytokine IL-1RA, and increased inflammatory ratios TNF-α/IL-10 and TNF-α/IL-1RA in older adults with obesity and T2D. Effect on CLIP was inversely related to baseline inflammatory status.