Elevated body mass index is associated with an increased risk of infectious disease admissions and mortality: a mendelian randomization study

A Genome-wide Association Study of Susceptibility to Upper Urinary Tract Infections

BACKGROUND

Our goal was to identify genetic and modifiable risk factors for upper urinary tract infections (UTIs).

METHODS

We used data from UK Biobank, the Trøndelag Health Study, and the Michigan Genomics Initiative to conduct genome-wide association studies and sex-stratified analyses on upper UTI. Mendelian randomization (MR) analyses were conducted to examine potential causal relationships between cardiometabolic risk factors and upper UTIs.

RESULTS

One genome-wide significant (P ≤ 5E-08) locus was associated with the susceptibility to upper UTI, located near TSN in the female-only analysis. Additionally, we identified suggestive (P ≤ 5E-06) loci near DNAI3 for females, SCAMP1-AS1 for males, and near TSN, LINC00603, and HLA-DQA2 for both sexes. In MR analyses, higher genetically predicted lifetime smoking scores were associated with an increased risk of developing upper UTI for females and both sexes (odds ratio [OR], 4.84, P = 4.50E-06 and OR, 2.79, P = 3.02E-05, respectively).

CONCLUSIONS

We found that genetic variants near TSN was associated with the risk of upper UTIs among females. In addition, we found several genetic loci with suggestive associations with the risk of upper UTIs. Finally, MR analyses found smoking to be a potential causal risk factor for upper UTIs.

Carnitine is causally associated with susceptibility and severity of sepsis: a Mendelian randomization study

Background

Energy metabolism disorders contribute to the development of sepsis. Carnitine is essential for fatty acid metabolism and energy production. Therefore, we aimed to explore whether there is a causal relationship between carnitine levels and sepsis.

Methods

Two-sample Mendelian randomization (MR) analysis was performed. The single nucleotide polymorphisms (SNPs) of carnitine from the genome-wide association (GWAS) study were used as exposure instrumental variables, and the susceptibility and severity of sepsis in the UK Biobank were used as outcomes. The inverse-variance weighted (IVW), MR-Egger, and weighted median methods were used to evaluate the causal relationship between exposure and outcomes. Heterogeneity was assessed using IVW and MR-Egger’s and Cochran’s Q tests, and pleiotropy was tested using the MR-Egger intercept and MR-PRESSO.

Results

Using the IVW method, a one-standard-deviation increase in genetically determined carnitine levels was found to be associated with increased susceptibility to sepsis in populations under 75 years of age (odds ratio [OR]: 2.696; 95% confidence interval [CI]: 1.127–6.452; P = 0.026) and increased severity of sepsis (OR: 22.31; 95% CI: 1.769–281.282; P = 0.016). Sensitivity analysis did not reveal heterogeneity or horizontal pleiotropy; therefore, the results indicated robustness.

Conclusion

Genetic susceptibility to increased carnitine levels in the blood may increase the susceptibility and severity of sepsis. Therefore, interventions at an early stage in patients with high carnitine levels may reduce the risk of developing sepsis.

Association of modifiable risk factors and infectious diseases among individuals with hypertension: a prospective cohort study

BACKGROUND

A comprehensive assessment of combined modifiable risk factors in relation to infectious diseases among individuals with hypertension is lacking, and the potential mechanisms of these associations remain unclear. To investigate the prospective associations of a combination of lifestyle behaviors and cardiometabolic factors with the risk of infectious diseases among individuals with hypertension and to estimate whether and to what extent blood biomarkers mediate these associations.

METHODS

This cohort study included 147,188 participants with hypertension and complete data on modifiable risk factors from the UK Biobank. Health score was constructed from eight modifiable risk factors, including four lifestyle behaviors (diet, physical activity, smoking, and sleep duration) and four cardiometabolic factors (body mass index, blood lipids, blood glucose, and blood pressure). Cox proportional hazards regression analysis was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the health score and infectious diseases. The mediation analysis was performed to assess the potential intermediation effects of blood biomarkers.

RESULTS

Over a median follow-up of 12.5 years, 27,398 participants with infectious diseases were documented, with 960 respiratory infectious diseases and 7940 digestive infectious diseases. After adjusting for potential confounders, the HR (95% CI) for the highest versus the lowest quartile of health score was 0.64 (0.62, 0.66) for infectious diseases, 0.72 (0.60, 0.86) for respiratory infectious diseases, and 0.66 (0.62, 0.71) for digestive infectious diseases. Stratified factors including duration of hypertension did not modify the associations between the health score and infectious diseases. In addition, biomarkers including inflammation and renal function collectively explained 46.60% of the associations between the combined lifestyle factors and infectious disease risk among individuals with hypertension.

CONCLUSIONS AND RELEVANCE

Ideal management of combined modifiable risk factors was associated with lower risks of infectious diseases and might produce profound changes in blood biomarkers among individuals with hypertension. Additionally, specific biomarkers appeared to serve as an intermediate between combined lifestyle factors and infectious diseases. These insights highlighted the important role of a combination of healthy lifestyle and favorable cardiometabolic status in reducing disease burden and facilitated the understanding of biological mechanisms underlying modifiable risk factors with infectious diseases.

Correlation between body mass index and gender-specific 28-day mortality in patients with sepsis: a retrospective cohort study

Objective

To investigate the potential correlation between body mass index (BMI) and the 28-day mortality rate among sepsis patients and the gender difference in this association.

Design

The current research was a retrospective cohort study.

Participants

A total of 14,883 male and female cohorts of sepsis patients were included in the Medical Information Mart for Intensive Care IV (MIMIC-IV V2.2) database. Patients in each gender cohort were further classified as underweight, normal weight, overweight, or obese according to BMI and the World Health Organization (WHO) BMI categories.

Outcomes

The 28-day mortality from the date of ICU hospitalization was the primary outcome measure.

Results

The BMI and 28-day mortality exhibited an L-shaped relationship (p for nonlinearity <0.001) with significant gender-specific differences. Subgroup analysis revealed different association patterns between the male and female cohorts. Specifically, BMI and mortality exhibited a U-shaped curve relationship among the males (p for nonlinearity <0.001) and an L-shaped relationship among the females (p for nonlinearity = 0.045).

Conclusion

This study proposes a link between extreme BMI and 28-day mortality in patients with sepsis. Underweight patients have an increased risk of mortality; however, this risk decreases in overweight and obese patients. Upon stratifying by sex, a U-shaped pattern was observed, indicating an association between BMI and 28-day mortality in males, while an L-shaped pattern emerged in females.

Association between body mass index and urinary tract infections: A cross-sectional investigation of the PERSIAN Guilan cohort study

Introduction

There is a relationship between excess body weight and the risk of a number of infectious diseases, including urinary tract infections (UTIs). This study aimed to investigate the correlation between body mass index (BMI) and UTIs among Prospective Epidemiological Research Studies of the Iranian Adults (PERSIAN) Guilan Cohort Study (PGCS) population.

Methods

This cross-sectional study was conducted on 10,520 individuals aged 35-70 years from PGCS. The demographical data and clinical characteristics of the participants were recorded. Microscopic examination of the urine samples was performed to detect the presence of bacteria or white blood cells (WBC) as indicators of infection. UTI was defined as the presence of bacteria in the urine (Few, moderate, and many) and a value of ≥10 WBC/high power field (HPF) by light microscopy.

Results

The prevalence of UTIs in this study was 8.8%, with a higher incidence in females compared to males (12.2% vs. 4.7%, p < 0.001). Among participants, the prevalence of UTIs across different weight categories was as follows: underweight/normal weight, 7.1%; overweight, 8.1%; and obesity, 10.9%. According to the unadjusted model, subjects with obesity were at significantly higher odds for UTIs than subjects with underweight/normal BMI (OR = 1.62, 95% CI: 1.35-1.93, p < 0.001). However, this association was no longer significant after adjusting for demographic and clinical variables.

Conclusion

The findings of this study provide evidence supporting a higher prevalence of UTIs among individuals with obesity.

The Mediating Roles of Lung Function Traits and Inflammatory Factors on the Associations between Measures of Obesity and Risk of Lower Respiratory Tract Infections: A Mendelian Randomization Study

BACKGROUND

Identifying mediators between obesity-related traits and lower respiratory tract infections (LRTIs) would inform preventive and therapeutic strategies to reduce the burden of LRITs. We aimed to recognize whether lung function and inflammatory factors mediate their associations.

METHODS

We conducted a two-step, two-sample Mendelian randomization (MR) analysis. Two-sample MR was performed on (1) obesity-related traits (i.e., body mass index [BMI], waist circumference [WC], and waist-to-hip ratio [WHR]) and LRTIs (i.e., acute bronchitis, acute bronchiolitis, bronchiectasis, influenza, and pneumonia), (2) obesity-related traits and potential mediators, and (3) potential mediators and LRTIs. Next, two-step MR was applied to infer whether the mediation effects exist.

RESULTS

We found that C-reactive protein (CRP), interleukin-6 (IL-6), and forced expiratory volume in the first second (FEV1) mediated 32.59% (95% CI: 17.90%, 47.27%), 7.96% (95% CI: 1.79%, 14.14%), and 4.04% (95% CI: 0.34%, 7.74%) of the effect of BMI on pneumonia, and they mediated 26.90% (95% CI: 13.98%, 39.83%), 10.23% (95% CI: 2.72%, 17.73%), and 4.67% (95% CI: 0.25%, 9.09%) of the effect of WC on pneumonia, respectively. Additionally, CRP, forced vital capacity (FVC), and FEV1 mediated 18.66% (95% CI: 8.70%, 28.62%), 8.72% (95% CI: 1.86%, 15.58%), and 8.41% (95% CI: 2.77%, 14.06%) of the effect of BMI on acute bronchitis, and they mediated 19.96% (95% CI: 7.44%, 32.48%), 12.19% (95% CI: 2.00%, 22.39%), and 12.61% (95% CI: 2.94%, 22.29%) of the effect of WC on acute bronchitis, respectively.

CONCLUSIONS

Health interventions linked to reducing inflammation and maintaining normal lung function could help mitigate the risk of obesity-related LRTIs.

Adiposity and mortality among intensive care patients with COVID-19 and non-COVID-19 respiratory conditions: a cross-context comparison study in the UK

BACKGROUND

Adiposity shows opposing associations with mortality within COVID-19 versus non-COVID-19 respiratory conditions. We assessed the likely causality of adiposity for mortality among intensive care patients with COVID-19 versus non-COVID-19 by examining the consistency of associations across temporal and geographical contexts where biases vary.

METHODS

We used data from 297 intensive care units (ICUs) in England, Wales, and Northern Ireland (Intensive Care National Audit and Research Centre Case Mix Programme). We examined associations of body mass index (BMI) with 30-day mortality, overall and by date and region of ICU admission, among patients admitted with COVID-19 (N = 34,701; February 2020-August 2021) and non-COVID-19 respiratory conditions (N = 25,205; February 2018-August 2019).

RESULTS

Compared with non-COVID-19 patients, COVID-19 patients were younger, less often of a white ethnic group, and more often with extreme obesity. COVID-19 patients had fewer comorbidities but higher mortality. Socio-demographic and comorbidity factors and their associations with BMI and mortality varied more by date than region of ICU admission. Among COVID-19 patients, higher BMI was associated with excess mortality (hazard ratio (HR) per standard deviation (SD) = 1.05; 95% CI = 1.03-1.07). This was evident only for extreme obesity and only during February-April 2020 (HR = 1.52, 95% CI = 1.30-1.77 vs. recommended weight); this weakened thereafter. Among non-COVID-19 patients, higher BMI was associated with lower mortality (HR per SD = 0.83; 95% CI = 0.81-0.86), seen across all overweight/obesity groups and across dates and regions, albeit with a magnitude that varied over time.

CONCLUSIONS

Obesity is associated with higher mortality among COVID-19 patients, but lower mortality among non-COVID-19 respiratory patients. These associations appear vulnerable to confounding/selection bias in both patient groups, questioning the existence or stability of causal effects.

Role of lifestyle factors on the development and long-term prognosis of pneumonia and cardiovascular disease in the Chinese population

BACKGROUND

Whether adherence to a healthy lifestyle is associated with a lower risk of developing pneumonia and a better long-term prognosis remains unclear. This study aimed to investigate associations of individual and combined lifestyle factors (LFs) with the incidence risk and long-term prognosis of pneumonia hospitalization.

METHODS

Using data from the China Kadoorie Biobank study, we used the multistate models to investigate the role of five high-risk LFs, including smoking, excessive alcohol drinking, unhealthy dietary habits, physical inactivity, and unhealthy body shape, alone or in combination in the transitions from a generally healthy state at baseline to pneumonia hospitalization or cardiovascular disease (CVD, regarded as a reference outcome), and subsequently to mortality.

RESULTS

Most of the five high-risk LFs were associated with increased risks of transitions from baseline to pneumonia and from pneumonia to death, but with different risk estimates. The greater the number of high-risk LFs, the higher the risk of developing pneumonia and long-term mortality risk after pneumonia, with the strength of associations comparable to that of LFs and CVD. Compared to participants with 0-1 high-risk LF, the adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for transitions from baseline to pneumonia and from pneumonia to death in those with five high-risk LFs were 1.43 (1.28-1.60) and 1.98 (1.61-2.42), respectively. Correspondingly, the respective HRs (95% CIs) for transitions from baseline to CVD and from CVD to death were 2.00 (1.89-2.11) and 1.44 (1.30-1.59), respectively. The risk estimates changed slightly when further adjusting for the presence of major chronic diseases.

CONCLUSION

In this Chinese population, unhealthy LFs were associated with an increased incidence and long-term mortality risk of pneumonia.

Acute kidney injury genetic risks: taking it 1 SNP at a time

This commentary addresses some of the strengths, shortcomings, and challenges of the genome-wide association study of acute kidney injury (AKI) report in this issue. This AKI genome-wide association study is well executed and provides significant progress in finding 2 genome-wide significant loci. However, significant interpretive challenges remain, where advancements in methods are needed because of the clinical heterogeneity of the AKI phenotype, plus possible bias due to genetic correlation between index hospitalization risk and AKI risk.

Development and validation of an interpretable machine learning for mortality prediction in patients with sepsis

Introduction

Sepsis is a leading cause of death. However, there is a lack of useful model to predict outcome in sepsis. Herein, the aim of this study was to develop an explainable machine learning (ML) model for predicting 28-day mortality in patients with sepsis based on Sepsis 3.0 criteria.

Methods

We obtained the data from the Medical Information Mart for Intensive Care (MIMIC)-III database (version 1.4). The overall data was randomly assigned to the training and testing sets at a ratio of 3:1. Following the application of LASSO regression analysis to identify the modeling variables, we proceeded to develop models using Extreme Gradient Boost (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) techniques with 5-fold cross-validation. The optimal model was selected based on its area under the curve (AUC). Finally, the Shapley additive explanations (SHAP) method was used to interpret the optimal model.

Results

A total of 5,834 septic adults were enrolled, the median age was 66 years (IQR, 54-78 years) and 2,342 (40.1%) were women. After feature selection, 14 variables were included for developing model in the training set. The XGBoost model (AUC: 0.806) showed superior performance with AUC, compared with RF (AUC: 0.794), LR (AUC: 0.782) and SVM model (AUC: 0.687). SHAP summary analysis for XGBoost model showed that urine output on day 1, age, blood urea nitrogen and body mass index were the top four contributors. SHAP dependence analysis demonstrated insightful nonlinear interactive associations between factors and outcome. SHAP force analysis provided three samples for model prediction.

Conclusion

In conclusion, our study successfully demonstrated the efficacy of ML models in predicting 28-day mortality in sepsis patients, while highlighting the potential of the SHAP method to enhance model transparency and aid in clinical decision-making.

Genetic correlations and causal relationships between cardio-metabolic traits and sepsis

Cardio-metabolic traits have been reported to be associated with the development of sepsis. It is, however, unclear whether these co-morbidities reflect causal associations, shared genetic heritability, or are confounded by environmental factors. We performed three analyses to explore the relationships between cardio-metabolic traits and sepsis. Mendelian randomization (MR) study to evaluate the causal effects of multiple cardio-metabolic traits on sepsis. Global genetic correlation analysis to explore the correlations between cardio-metabolic traits and sepsis. Local genetic correlation (GC) analysis to explore shared genetic heritability between cardio-metabolic traits and sepsis. Some loci were further examined for related genes responsible for the causal relationships. Genetic associations were obtained from the UK Biobank data or published large-scale genome-wide association studies with sample sizes between 200,000 to 750,000. In MR, we found causality between BMI and sepsis (OR: 1.53 [1.4-1.67]; p < 0.001). Body mass index (BMI), which is confirmed by sensitivity analyses and multivariable MR adjusting for confounding factors. Global GC analysis showed a significant correlation between BMI and sepsis (rg = 0.55, p < 0.001). More cardio-metabolic traits were identified to be correlated to the sepsis onset such as CRP (rg = 0.37, p = 0.035), type 2 diabetes (rg = 0.33, p < 0.001), HDL (rg = - 0.41, p < 0.001), and coronary artery disease (rg = 0.43, p < 0.001). Local GC revealed some shared genetic loci responsible for the causality. The top locus 1126 was located at chromosome 7 and comprised genes HIBADH, JAZF1, and CREB5. The present study provides evidence for an independent causal effect of BMI on sepsis. Further detailed analysis of the shared genetic heritability between cardio-metabolic traits and sepsis provides the opportunity to improve the preventive strategies for sepsis.

Evaluating the risk of sepsis attributing to obesity: a two-sample Mendelian randomization study

BACKGROUND

Sepsis is a reaction to infection with high morbidity and mortality. It has been noted that patients with obesity were more likely to suffer from sepsis. However, the causality remains elucidating, as reverse causality and residual confounding could not be largely mitigated in conventional observational studies.

OBJECTIVE

To evaluate the risk of sepsis attributed to obesity phenotypes including body mass index (BMI), waist circumstance (WC), and WC adjusted for BMI (WCadjBMI) in a causal way.

METHODS

We conducted a two-sample Mendelian randomization (MR) study using large-scale genome-wide association study summary data with sample sizes ranging from 231 353 to 486 484. The inverse-weighted variance (IVW) was conducted as the primary approach. We also used the weighted median and MR-Egger for causal inference. A sensitivity analysis was conducted to evaluate the reliability of the MR estimates.

RESULTS

IVW detected that genetic liability for increased BMI [odds ratio (OR) = 1.40, 95% confidence interval (CI) = 1.20-1.63, P = 1.52 × 10-5] and WC (OR = 1.02, 95% CI = 1.01-1.03, P = 4.28 × 10-3) predicted a higher risk of sepsis. No evidence was observed for a causal effect of WCadjBMI on sepsis risk (OR = 1.01, 95% CI = 1.00-1.02, P = 0.08). Sensitivity analysis did not identify any bias in the MR results.

CONCLUSION

This MR study showed that obesity contributed to an increased risk of sepsis, indicating that obesity management might be beneficial for reducing sepsis risk. Key messages What is already known on this topic-Observational studies have reported the association between obesity and sepsis, but the causality has not been determined. What this study adds-This Mendelian randomization study demonstrated that obesity-related phenotypes, including body mass index and Waist circumstance, causally increased the risk of sepsis. How this study might affect research, practice, or policy-The findings of our study might have an implication for clinicians that obesity management might be a promised strategy for reducing the risk of sepsis.

Causal relationship between blood metabolites and risk of five infections: a Mendelian randomization study

OBJECTIVE

Infectious diseases continue to pose a significant threat in the field of global public health, and our understanding of their metabolic pathogenesis remains limited. However, the advent of genome-wide association studies (GWAS) offers an unprecedented opportunity to unravel the relationship between metabolites and infections.

METHODS

Univariable and multivariable Mendelian randomization (MR) was commandeered to elucidate the causal relationship between blood metabolism and five high-frequency infection phenotypes: sepsis, pneumonia, upper respiratory tract infections (URTI), urinary tract infections (UTI), and skin and subcutaneous tissue infection (SSTI). GWAS data for infections were derived from UK Biobank and the FinnGen consortium. The primary analysis was conducted using the inverse variance weighted method on the UK Biobank data, along with a series of sensitivity analyses. Subsequently, replication and meta-analysis were performed on the FinnGen consortium data.

RESULTS

After primary analysis and a series of sensitivity analyses, 17 metabolites were identified from UK Biobank that have a causal relationship with five infections. Upon joint analysis with the FinGen cohort, 7 of these metabolites demonstrated consistent associations. Subsequently, we conducted a multivariable Mendelian randomization analysis to confirm the independent effects of these metabolites. Among known metabolites, genetically predicted 1-stearoylglycerol (1-SG) (odds ratio [OR] = 0.561, 95% confidence interval [CI]: 0.403-0.780, P < 0.001) and 3-carboxy-4-methyl-5-propyl-2-furanpropanoate (CMPF) (OR = 0.780, 95%CI: 0.689-0.883, P < 0.001) was causatively associated with a lower risk of sepsis, and genetically predicted phenylacetate (PA) (OR = 1.426, 95%CI: 1.152-1.765, P = 0.001) and cysteine (OR = 1.522, 95%CI: 1.170-1.980, P = 0.002) were associated with an increased risk of UTI. Ursodeoxycholate (UDCA) (OR = 0.906, 95%CI: 0.829-0.990, P = 0.029) is a protective factor against pneumonia. Two unknown metabolites, X-12407 (OR = 1.294, 95%CI: 1.131-1.481, P < 0.001), and X-12847 (OR = 1.344, 95%CI: 1.152-1.568, P < 0.001), were also identified as independent risk factors for sepsis.

CONCLUSIONS

In this MR study, we demonstrated a causal relationship between blood metabolites and the risk of developing sepsis, pneumonia, and UTI. However, there was no evidence of a causal connection between blood metabolites and the risk of URTI or SSTI, indicating a need for larger-scale studies to further investigate susceptibility to certain infection phenotypes.

The relationship between high density lipoprotein cholesterol and sepsis: A clinical and genetic approach

Sepsis accounts for one in three hospital deaths. Higher concentrations of high-density lipoprotein cholesterol (HDL-C) are associated with apparent protection from sepsis, suggesting a potential therapeutic role for HDL-C or drugs, such as cholesteryl ester transport protein (CETP) inhibitors that increase HDL-C. However, these beneficial clinical associations might be due to confounding; genetic approaches can address this possibility. We identified 73,406 White adults admitted to Vanderbilt University Medical Center with infection; 11,612 had HDL-C levels, and 12,377 had genotype information from which we constructed polygenic risk scores (PRS) for HDL-C and the effect of CETP on HDL-C. We tested the associations between predictors (measured HDL-C, HDL-C PRS, CETP PRS, and rs1800777) and outcomes: sepsis, septic shock, respiratory failure, and in-hospital death. In unadjusted analyses, lower measured HDL-C concentrations were significantly associated with increased risk of sepsis (p = 2.4 × 10-23 ), septic shock (p = 4.1 × 10-12 ), respiratory failure (p = 2.8 × 10-8 ), and in-hospital death (p = 1.0 × 10-8 ). After adjustment (age, sex, electronic health record length, comorbidity score, LDL-C, triglycerides, and body mass index), these associations were markedly attenuated: sepsis (p = 2.6 × 10-3 ), septic shock (p = 8.1 × 10-3 ), respiratory failure (p = 0.11), and in-hospital death (p = 4.5 × 10-3 ). HDL-C PRS, CETP PRS, and rs1800777 significantly predicted HDL-C (p < 2 × 10-16 ), but none were associated with sepsis outcomes. Concordant findings were observed in 13,254 Black patients hospitalized with infections. Lower measured HDL-C levels were significantly associated with increased risk of sepsis and related outcomes in patients with infection, but a causal relationship is unlikely because no association was found between the HDL-C PRS or the CETP PRS and the risk of adverse sepsis outcomes.

Exploring the Causality Between Body Mass Index and Sepsis: A Two-Sample Mendelian Randomization Study

Objective: Observational epidemiological studies have shown a link between obesity and sepsis, but any causal relationship is not clear. Our study aimed to explore the correlation and causal relationship between body mass index and sepsis by a two-sample Mendelian randomization (MR). Methods: In large sample genome-wide association studies, single-nucleotide polymorphisms related to body mass index were screened as instrumental variables. Three MR methods, MR-Egger regression, weighted median estimator, and inverse variance-weighted, were used to evaluate the causal relationship between body mass index and sepsis. Odds ratio (OR) and 95% confidence interval (CI) were used as the evaluation index of causality, and sensitivity analyses were conducted to assess pleiotropy and instrument validity. Results: By two-sample MR, the inverse variance weighting method results suggested that increased body mass index was associated with an increased risk of sepsis (odds ratio 1.32; 95% CI 1.21-1.44; p = 1.37 × 10^-9) and streptococcal septicemia (OR 1.46; 95% CI 1.11-1.91; p = 0.007), but there was no causal relationship with puerperal sepsis (OR, 1.06; 95% CI, 0.87-1.28; p = 0.577). Sensitivity analysis was consistent with the results, and there was no heterogeneity and level of pleiotropy. Conclusion: Our study supports a causal relationship between body mass index and sepsis. Proper control of body mass index may prevent sepsis.

Risk of lower respiratory tract infections: a genome-wide association study with Mendelian randomization analysis in three independent European populations

OBJECTIVE

Lower respiratory tract infections (LRTIs) are a leading cause of morbidity and mortality worldwide. Few studies have previously investigated genetic susceptibility and potential risk factors for LRTI.

METHODS

We used data from the UK Biobank, Trøndelag Health Study (HUNT), and FinnGen to conduct a genome-wide association study (GWAS). Cases were subjects hospitalized with LRTI, and controls were subjects with no such hospitalization. We conducted stratification and interaction analyses to evaluate whether the genetic effect of LRTI differed by sex or smoking. Mendelian randomization (MR) analyses were conducted to identify the unconfounded relationship between cardiometabolic risk factors and LRTI.

RESULTS

A total of 25 320 cases and 575 294 controls were included. The 15q25.1 locus reached genome-wide significance in the meta-analysis (rs10519203: OR 0.94, p 3.87e-11). The protective effect of effect allele of rs10519203 was present among smokers (OR 0.90, 95%CI 0.87-0.92, p 1.38e-15) but not among never-smokers (OR 1.01, 95%CI 0.97-1.06, p 5.20e-01). In MR analyses, we found that increasing body mass index (OR 1.31, 95%CI 1.24-1.40, p 3.78e-18), lifetime smoking (OR 2.83, 95%CI 2.34-3.42, p 6.56e-27), and systolic blood pressure robustly increased the risk of LRTIs (OR 1.11, 95%CI 1.02-1.22, p 1.48e-02).

CONCLUSION

A region in 15q25.1 was strongly associated with LRTI susceptibility. Reduction in the prevalence of smoking, overweight, obesity, and hypertension may reduce the disease burden of LRTIs.

No Casual Relationship Between T2DM and the Risk of Infectious Diseases: A Two-Sample Mendelian Randomization Study

BACKGROUND

In epidemiological studies, it has been proven that the occurrence of type 2 diabetes mellitus (T2DM) is related to an increased risk of infectious diseases. However, it is still unclear whether the relationship is casual.

METHODS

We employed a two-sample Mendelian randomization (MR) to clarify the causal effect of T2DM on high-frequency infectious diseases: sepsis, skin and soft tissue infections (SSTIs), urinary tract infections (UTIs), pneumonia, and genito-urinary infection (GUI) in pregnancy. And then, we analyzed the genome-wide association study (GWAS) meta-analysis of European-descent individuals and conducted T2DM-related single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) that were associated with genome-wide significance (p < 5 × 10-8). MR estimates were obtained using the inverse variance-weighted (IVW), the MR-Egger regression, the simple mode (SM), weighted median, and weighted mode.

RESULTS

The UK Biobank (UKB) cohort (n > 500,000) provided data for GWASs on infectious diseases. MR analysis showed little evidence of a causal relationship of T2DM with five mentioned infections' (sepsis, SSTI, UTI, pneumonia, and GUI in pregnancy) susceptibility [odds ratio (OR) = 0.99999, p = 0.916; OR = 0.99986, p = 0.233; OR = 0.99973, p = 0.224; OR = 0.99997, p = 0.686; OR, 1.00002, p = 0.766]. Sensitivity analysis showed similar results, indicating the robustness of causality. There were no heterogeneity and pleiotropic bias.

CONCLUSION

T2DM would not be causally associated with high-frequency infectious diseases (including sepsis, SSTI, UTI, pneumonia, and GUI in pregnancy).

The effect of angiotensin-converting enzyme levels on COVID-19 susceptibility and severity: a Mendelian randomization study

Background

There has been uncertainty about the safety or benefit of angiotensin-converting enzyme (ACE) inhibitors during the COVID-19 pandemic. We used Mendelian randomization using genetic determinants of serum-ACE levels to test whether decreased ACE levels increase susceptibility to SARS-CoV-2 infection or COVID-19 severity, while reducing potential bias from confounding and reverse causation in observational studies.

Methods

Genetic variants strongly associated with ACE levels, which were nearby the ACE gene, were identified from the ORIGIN trial and a separate genome-wide association study (GWAS) of ACE levels from the AGES cohort. The ORIGIN trial included 4147 individuals of European and Latino ancestries. Sensitivity analyses were performed using a study of 3200 Icelanders. Cohorts from the COVID-19 Host Genetics Initiative GWAS of up to 960 186 individuals of European ancestry were used for COVID-19 susceptibility, hospitalization and severe-disease outcome.

Results

Genetic variants were identified that explain between 18% and 37% of variance in ACE levels. Using genetic variants from the ORIGIN trial, a standard-deviation decrease in ACE levels was not associated with an increase in COVID-19 susceptibility [odds ratio (OR): 1.02, 95% confidence interval (CI): 0.90, 1.15], hospitalization (OR: 0.86, 95% CI: 0.68, 1.08) or severe disease (OR: 0.74, 95% CI: 0.51, 1.06). Using genetic variants from the AGES cohort, the result was similar for susceptibility (OR: 0.98, 95% CI: 0.89, 1.09), hospitalization (OR: 0.86, 95% CI: 0.66, 1.11) and severity (OR: 0.75, 95% CI: 0.50, 1.14). Multiple-sensitivity analyses led to similar results.

Conclusion

Genetically decreased serum ACE levels were not associated with susceptibility to, or severity of, COVID-19 disease. These data suggest that individuals taking ACE inhibitors should not discontinue therapy during the COVID-19 pandemic.

Genetic Determinants of Antibody-Mediated Immune Responses to Infectious Diseases Agents: A Genome-Wide and HLA Association Study

BACKGROUND

Infectious diseases are causally related to a large array of noncommunicable diseases (NCDs). Identifying genetic determinants of infections and antibody-mediated immune responses may shed light on this relationship and provide therapeutic targets for drug and vaccine development.

METHODS

We used the UK biobank cohort of up to 10 000 serological measurements of infectious diseases and genome-wide genotyping. We used data on 13 pathogens to define 46 phenotypes: 15 seropositivity case-control phenotypes and 31 quantitative antibody measurement phenotypes. For each of these, we performed genome-wide association studies (GWAS) using the fastGWA linear mixed model package and human leukocyte antigen (HLA) classical allele and amino acid residue associations analyses using Lasso regression for variable selection.

RESULTS

We included a total of 8735 individuals for case-control phenotypes, and an average (range) of 4286 (276-8555) samples per quantitative analysis. Fourteen of the GWAS yielded a genome-wide significant (P < 5 ×10-8) locus at the major histocompatibility complex (MHC) on chromosome 6. Outside the MHC, we found a total of 60 loci, multiple associated with Epstein-Barr virus (EBV)-related NCDs (eg, RASA3, MED12L, and IRF4). FUT2 was also identified as an important gene for polyomaviridae. HLA analysis highlighted the importance of DRB1*09:01, DQB1*02:01, DQA1*01:02, and DQA1*03:01 in EBV serologies and of DRB1*15:01 in polyomaviridae.

CONCLUSIONS

We have identified multiple genetic variants associated with antibody immune response to 13 infections, many of which are biologically plausible therapeutic or vaccine targets. This may help prioritize future research and drug development.

The Association Between Body Mass Index and the Risk of Hospitalization and Mortality due to Infection: A Prospective Cohort Study

Background

We aim to determine whether obesity increases the risk of various infections using a large prospective population-based cohort.

Methods

A total of 120 864 adults were recruited from the New Taipei City health screening program from 2005 to 2008. Statistics for hospitalization and mortality due to infection were obtained from the National Health Insurance Database and the National Death Registry in Taiwan.

Results

During a mean follow-up period of 7.61 years, there were 438, 7582, 5298, and 1480 first hospitalizations due to infection in the underweight, normal, overweight, and obese groups, respectively. Obesity significantly increases the risk of hospitalization for intra-abdominal infections (adjusted hazard ratio [aHR], 1.19; 95% CI, 1.00–1.40), including diverticulitis, liver abscess, acute cholecystitis and anal and rectal abscess, reproductive and urinary tract infection (aHR, 1.38; 95% CI, 1.26–1.50), skin and soft tissue infection (aHR, 2.46; 95% CI, 2.15–2.81), osteomyelitis (aHR, 1.70; 95% CI, 1.14–2.54), and necrotizing fasciitis (aHR, 3.54; 95% CI,1.87–6.67), and this relationship is dose-dependent. This study shows that there is a U-shaped association between body mass index (BMI) and hospitalization for lower respiratory tract infection, septicemia, and the summation of all infections and that underweight people are at the greatest risk, followed by obese people. There is a clear negative relationship between BMI and infection-related mortality.

Conclusions

The pattern that BMI affects the risk of hospitalization and mortality due to infection varies widely across infection sites. It is necessary to tailor preventive and therapeutic measures against different infections in hosts with different BMIs.