Conventional Epidemiological Studies Research Articles

Ambient air pollution and risk of allergic respiratory diseases in European and East Asian populations: A Mendelian randomization study

BackgroundAmbient air pollution has become a challenging global health issue since industrialization, especially affecting respiratory diseases. However, the causal link between air pollution and allergic respiratory diseases (ARDs) remains unclear due to confounding factors in conventional epidemiological studies across different populations. Thus, we aimed to clarify the causal associations between air pollution and ARDs in European and East Asian populations using Mendelian randomization (MR). MethodsMR utilizes genetic variants and provides a satisfactory level of causal evidence. Genetic data for exposures (PM2.5, PM2.5 absorbance, PM10, PMcoarse, NO2 and NOx) and outcomes (allergic rhinitis, chronic rhinosinusitis, asthma, and obesity related asthma) were obtained from genome-wide association studies. Instrumental variables were strictly filtered based on core assumptions. Two-sample MR and sensitivity analyses were conducted separately for European and East Asian populations. ResultsPMcoarse was causally associated with an increased risk of chronic rhinosinusitis (OR = 1.588 [1.002–2.518]; p = 0.049) and obesity related asthma (OR = 1.956 [1.012–3.780]; p = 0.046) in European population, and PM10 was associated with a decreased risk of allergic rhinitis in East Asian population (OR = 0.882 [0.798–0.974]; p = 0.013). No heterogeneity or pleiotropy was detected in any significant causal association. ConclusionOur findings indicate that ambient air pollution has opposite impacts on the etiology of ARDs in European and East Asian populations, which provides evidence for decisions on public policies and suggests that different responses to environmental factors such as air pollution may contribute to racial heterogeneity of ARDs.

A genetically informed study reveals modifiable pathways in skin cancer

BackgroundIdentifying modifiable risk factors is essential for the prevention of skin cancer; however, establishing causality can be challenging in conventional epidemiological studies. This study aimed to determine the causal associations of potentially modifiable risk factors with skin cancer using Mendelian randomization (MR).MethodsGenetic instruments for 53 risk factors, including socioeconomic status, dietary and lifestyle factors, anthropometric measures, medication use, and comorbidities, were identified from previous genome-wide association studies. Two-sample MR analyses were performed using summary statistics for three major types of skin cancer: melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). Findings were verified using multiple MR methods under different assumptions and replication datasets.ResultsGenetic liability to sunburn occasions, actinic keratosis, and prior skin cancers was associated with a higher risk of all three types of skin cancer, whereas genetic liability to vitiligo was associated with a lower risk. For specific skin cancer types, genetically predicted higher nevus counts and occupational class were associated with an increased risk of melanoma. Genetic liability to rheumatoid arthritis, type 2 diabetes, and increased physical activity were associated with a lower risk of BCC. Genetically predicated body mass index showed a negative association with BCC, and a positive association with SCC.ConclusionsOur study reaffirmed several previously established risk factors and identified novel potential risk factors for skin cancer. Further work is needed to unravel the biological pathways in different skin cancer types and translate our findings to inform public health policies.

Association between alcohol consumption and incidence of dementia in current drinkers: linear and non-linear mendelian randomization analysis

Previous conventional epidemiological studies found a J-shape relationship between alcohol consumption and dementia, but this result was subject to confounding biases and reverse causation. Therefore, we aimed to investigate the potential linear or non-linear causal association between alcohol consumption and the incident risk of dementia in current drinkers. This study used data from the UK Biobank to investigate the relationship between alcohol consumption and dementia risk. 313,958 White British current drinkers, who were free of dementia during 2006-2010, were followed up until 2021. Alcohol consumption was self-reported and calculated according to the National Health Service guideline. The primary outcome was all-cause dementia identified through hospital and mortality records. We used multivariable Cox models with restricted cubic splines for conventional analysis and both non-linear and linear Mendelian Randomization (MR) analyses to assess causal relationships, employing a genetic score based on 95 SNPs identified from a meta-genome-wide association study of 941,280 people from Europe. 313,958 current drinkers consumed an average of 13.6 [IQR: 7.1-25.2] units/week alcohol (men averaged 20.2 [11.1-33.9] units/week and women 9.5 [5.3-16.7] units/week). During a mean follow-up of 13.2 years, 5394 (1.7%) developed dementia. Multivariable Cox model with restricted cubic spline functions identified a J-shaped relationship between alcohol consumption and dementia risk, with the lowest risk at 12.2 units/week. The non-linear MR failed to identify a significant non-linear causal relationship (p=0.45). Both individual-level (HR: 2.22 95%CI [1.06-4.66]) and summary-level (1.89 [1.53-2.32]) linear MR analyses indicated that higher genetically predicted alcohol consumption increased dementia risk. This study identified a positive linear causal relationship between alcohol consumption and dementia among current drinkers. The J-shaped association found in conventional epidemiological analysis was not supported by non-linear MR analyses. Our findings suggested that there was no safe level of alcohol consumption for dementia. The Shenzhen Science and Technology Program and the Strategic Priority Research Program of Chinese Academy of Sciences.

No causal associations of genetically predicted birth weight and life course BMI with thyroid function and diseases.

Observational studies have suggested associations of birth weight, childhood BMI, and adulthood BMI with thyroid function or diseases. However, the causal relationships remain unclear due to residual confounding inherent in conventional epidemiological studies. We performed a two-sample Mendelian randomization (MR) study to investigate causal relationships of genetically predicted birth weight, childhood BMI, and adulthood BMI with a range of clinically relevant thyroid outcomes. Additionally, we conducted a reverse MR analysis on adulthood BMI. Data on exposures and outcomes were obtained from large-scale genome-wide association study meta-analyses predominantly composed of individuals of European ancestry. The MR analysis revealed no evidence of causal associations of birth weight or BMI at different life stages with thyrotropin (TSH) levels, hypothyroidism, hyperthyroidism, autoimmune thyroid disorders, or thyroid cancer. Contrarily, thyroid cancer demonstrated a significant causal relationship with increased adulthood BMI (β = 0.010, 95% CI: 0.006-0.015; p = 5.21 × 10-6). Our comprehensive MR did not find causal links of birth weight, childhood BMI, or adulthood BMI with thyroid diseases but provided evidence that thyroid cancer may play a role in weight gain. Our research findings offer valuable insights into the intricate relationship between body weight and thyroid health throughout an individual's life.

Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects.

Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. We developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding genes were detected in PCa patients in The Cancer Genome Atlas (TCGA). Genes encoding these proteins were significantly involved in cancer-related pathways. We further identified drugs targeting the identified proteins, which may serve as candidates for drug repurposing for treating PCa. In conclusion, this study identifies novel protein biomarker candidates for PCa risk, which may provide new perspectives on the etiology of PCa and improve its therapeutic strategies.

Circulating macrophage colony-stimulating factor levels and stroke: A Mendelian randomization study

ObjectivesColony-stimulating factor 1 (CSF1), also known as macrophage colony-stimulating factor, has been shown to be associated with risk of ischemic stroke in conventional epidemiological study. We performed a Mendelian randomization analysis to evaluate the effects of genetically predicted circulating CSF1 levels on stroke and carotid intima-media thickness (cIMT). MethodsGenetic variants robustly associated with CSF1 levels, located in the vicinity of the CSF1 gene (cis), were used as instruments for CSF1 levels. Genetic association estimates for ischemic stroke and its subtypes, intra-cerebral hemorrhage (ICH), and cIMT were obtained from MEGASTROKE (60,341 cases and 454,450 controls), ISGC (1,545 cases and 1,481 controls), and UK Biobank (22,179 individuals), respectively. ResultsGenetically predicted higher CSF1 levels was significantly associated with a higher risk of any ischemic stroke, large artery stroke (LAS) and cardioembolic stroke (CES), but not with small vessel stroke (SVS) and ICH. The odds ratios (ORs) per genetically predicted one standard deviation (SD) increase in circulating CSF1 levels were 1.11 (95% CI 1.04–1.17) for any ischemic stroke, 1.23 (95% CI 1.07–1.42) for LAS, 1.18 (95% CI 1.05–1.33) for CES, 1.07 (95% CI 0.94–1.21) for SVS, and 1.15 (95% CI 0.73–1.83) for ICH. Similarly, we also found that genetically predicted higher CSF1 levels were associated with higher cIMT, as a measure of subclinical atherosclerosis (cIMT, β 0.016, 95% CI, 0.004-0.029). ConclusionsThis study provides evidence that genetically predicted higher CSF1 levels was associated with higher risk of any ischemic stroke, LAS, and CES. Whether targeting CSF1 or its receptors can reduce the risk of ischemic stroke needs further study.

Pan-cancer mutational signature surveys correlated mutational signature with geospatial environmental exposures and viral infections

BackgroundCancer has been disproportionally affecting minorities. Genomic-based cancer disparity analyses have been less common than conventional epidemiological studies. In the past decade, mutational signatures have been established as characteristic footprints of endogenous or exogenous carcinogens. MethodsIntegrating datasets of diverse cancer types from The Cancer Genome Atlas and geospatial environmental risks of the registry hospitals from the United States Environmental Protection Agency, we explored mutational signatures from the aspect of racial disparity concerning pollutant exposures. The raw geospatial environmental exposure data were refined to 449 air pollutants archived and modeled from 2007 to 2017 and aggregated to the census county level. Additionally, hepatitis B and C viruses and human papillomavirus infection statuses were incorporated into analyses for skin cancer, cervical cancer, and liver cancer. ResultsMutation frequencies of key oncogenic genes varied substantially between different races. These differences were further translated into differences in mutational signatures. Survival analysis revealed that the increased pollution level is associated with worse survival. The analysis of the oncogenic virus revealed that aflatoxin, an affirmed carcinogen for liver cancer, was higher in Asian liver cancer patients than in White patients. The aflatoxin mutational signature was exacerbated by hepatitis infection for Asian patients but not for White patients, suggesting a predisposed genetic or genomic disadvantage for Asians concerning aflatoxin. ConclusionsEnvironmental pollutant exposures increase a mutational signature level and worsen cancer prognosis, presenting a definite adverse risk factor for cancer patients.

New Methods of Evaluating Health Effects of Combined Exposures to Chemicals and Their Problems to Be Solved

There are several basic prerequisites for the risk assessment of combined exposures to pesticides and dioxins using human health effects as the endpoint. First, all the target chemical substances exert the same toxicity to humans through the same mechanisms. Second, there is a linear dose-response relationship between the toxicity and effects of individual chemicals. With these two prerequisites, the effects of combined exposures are estimated as the sum of the toxicities of individual chemicals. For example, the toxicities of dioxins are calculated using their toxic equivalent quantities (TEQ) by considering the assigned toxic equivalent factor (TEF) of 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD) set individually from their isomers and homologs. In conventional epidemiological studies, when the impact of each of multiple chemical substances is examined, methods such as multiple regression analysis or using a generalized linear model (GLM) have been used on the basis of the same prerequisites. However, in practice, some of the chemicals exhibit collinearity in their effects or do not show a linear dose-response relationship. In recent years, there have been several methods developed in the field of machine learning being applied to epidemiological research. Typical examples were methods using Bayesian kernel machine regression (BKMR) and weighted quantile sum (WQS), and the shrinkage method, i.e., using the least absolute shrinkage and selection operator (Lasso) and elastic network model (ENM). In the future, while taking into account the findings of experimental studies in biology, epidemiology, and other fields, it is expected that various methods will be applied and selected.

Fibroblast Growth Factor-23 and Risk of Cardiovascular Diseases: A Mendelian Randomization Study.

Fibroblast growth factor-23 (FGF-23) is associated with a range of cardiovascular and noncardiovascular diseases in conventional epidemiological studies, but substantial residual confounding may exist. Mendelian randomization approaches can help control for such confounding. SCALLOP Consortium data of 19,195 participants were used to generate an FGF-23 genetic score. Data from 337,448 UK Biobank participants were used to estimate associations between higher genetically predicted FGF-23 concentration and the odds of any atherosclerotic cardiovascular disease (n=26,266 events), nonatherosclerotic cardiovascular disease (n=12,652), and noncardiovascular diseases previously linked to FGF-23. Measurements of carotid intima-media thickness and left ventricular mass were available in a subset. Associations with cardiovascular outcomes were also tested in three large case-control consortia: CARDIOGRAMplusC4D (coronary artery disease, n=181,249 cases), MEGASTROKE (stroke, n=34,217), and HERMES (heart failure, n=47,309). We identified 34 independent variants for circulating FGF-23, which formed a validated genetic score. There were no associations between genetically predicted FGF-23 and any of the cardiovascular or noncardiovascular outcomes. In UK Biobank, the odds ratio (OR) for any atherosclerotic cardiovascular disease per 1-SD higher genetically predicted logFGF-23 was 1.03 (95% confidence interval [95% CI], 0.98 to 1.08), and for any nonatherosclerotic cardiovascular disease, it was 1.01 (95% CI, 0.94 to 1.09). The ORs in the case-control consortia were 1.00 (95% CI, 0.97 to 1.03) for coronary artery disease, 1.01 (95% CI, 0.95 to 1.07) for stroke, and 1.00 (95% CI, 0.95 to 1.05) for heart failure. In those with imaging, logFGF-23 was not associated with carotid or cardiac abnormalities. Genetically predicted FGF-23 levels are not associated with atherosclerotic and nonatherosclerotic cardiovascular diseases, suggesting no important causal link. This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_01_10_CJN05080422.mp3.

Causal attribution fractions, and the attribution of smoking and BMI to the landscape of disease incidence in UK Biobank

Unlike conventional epidemiological studies that use observational data to estimate “associations” between risk factors and disease, the science of causal inference has identified situations where causal estimates can be made from observational data, using results such as the “backdoor criteria”. Here these results are combined with established epidemiological methods, to calculate simple population attribution fractions that estimate the causal influence of risk factors on disease incidence, and can be estimated using conventional proportional hazards methods. A counterfactual argument gives an attribution fraction for individuals. Causally meaningful attribution fractions cannot be constructed for all risk factors or confounders, but they can for the important established risk factors of smoking and body mass index (BMI). Using the new results, the causal attribution of smoking and BMI to the incidence of 226 diseases in the UK Biobank are estimated, and summarised in terms of disease chapters from the International Classification of Diseases (ICD-10). The diseases most strongly attributed to smoking and BMI are identified, finding 11 with attribution fractions greater than 0.5, and a small number with protective associations. The results provide new tools to quantify the causal influence of risk factors such as smoking and BMI on disease, and survey the causal influence of smoking and BMI on the landscape of disease incidence in the UK Biobank population.

Data science approaches to confronting the COVID-19 pandemic: a narrative review.

During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale ‘big data’ generated and harnessed for combating the COVID-19 pandemic. In this paper, we review the newly born data science approaches to confronting COVID-19, including the estimation of epidemiological parameters, digital contact tracing, diagnosis, policy-making, resource allocation, risk assessment, mental health surveillance, social media analytics, drug repurposing and drug development. We compare the new approaches with conventional epidemiological studies, discuss lessons we learned from the COVID-19 pandemic, and highlight opportunities and challenges of data science approaches to confronting future infectious disease epidemics.This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

Estimating the influence of body mass index (BMI) on mortality using offspring BMI as an instrumental variable.

ObjectiveHigh body mass index (BMI) is an important predictor of mortality but estimating underlying causality is hampered by confounding and pre-existing disease. Here we use information from the offspring to approximate parental BMIs, with an aim to avoid biased estimation of mortality risk caused by reverse causality.MethodsThe analyses were based on information on 9 674 offspring-mother, and 9 096 offspring-father pairs obtained from the 1958 British birth cohort. Parental BMI - mortality associations were analysed using conventional methods, and using offspring BMI as a proxy, or instrument, for their parents’ BMI.ResultsIn the conventional analysis, associations between parental BMI and all-cause mortality were U-shaped (Pcurvature <0.001), while offspring BMI had linear associations with parental mortality (Ptrend<0.001, Pcurvature>0.46). Curvature was particularly pronounced for mortality from respiratory diseases and from lung cancer. Instrumental variable analyses suggested a positive association between BMI and mortality from all causes [Mothers: HR per SD of BMI 1.43 (95% CI 1.21 to 1.69). Fathers: HR 1.17, (1.00 to 1.36)] and from coronary heart disease [Mothers: HR 1.65 (1.15 to 2.36). Fathers: HR 1.51, (1.17 to 1.97)]. These were larger than HR from the equivalent conventional analyses, despite some attenuation by adjustment for social indicators and smoking.ConclusionsAnalyses using offspring BMI as a proxy for parental BMI suggest that the apparent adverse consequences of low BMI are considerably overestimated and adverse consequences of overweight are underestimated in conventional epidemiological studies.

Screening of apathetic elderly adults using kinematic information in gait and sit-to-stand/stand-to-sit movements measured with Doppler radar.

This paper presents a Doppler radar apathy-screening technique applied to elderly people based on their basic daily activities of walking and movements of sit-to-stand and stand-to-sit (STS). Our Doppler radar system remotely measured the kinematic parameters of the movements of 78 community-dwelling elderly adults (27 apathetic participants and 51 non-apathetic ones). Subsequently, logistic regression models using the measured kinematic parameters of gait and sit-to-stand/stand-to-sit movements were constructed for screening. The experimental results verified that, although the model using gait parameters could screen an apathetic group with a sensitivity of 85.2% and a specificity of 58.8%, the model using the STS parameters achieved better screening accuracies with a sensitivity of 88.9% and a specificity of 76.5%. These results reveal that the kinematic information of STS movements is significantly more effective at detecting apathy than is the gait information, which is otherwise regarded to be effective in conventional epidemiological studies.

Association between telomere length and Parkinson's disease: a Mendelian randomization study

In this study, we examined the potential association of telomere length with Parkinson's disease (PD) using the publicly available genome-wide association study summary statistics from the International Parkinson's Disease Genomics Consortium involving up to 37,688 patients with PD and 449,056 controls in Mendelian randomization framework. The Mendelian randomization approach has the potential to investigate a causal relationship between a risk factor and a disease, avoiding confounding and reverse causation that often present in conventional epidemiological studies. We did not find that longer telomeres were associated with higher risks of PD (odds ratio: 1.18, 95% confidence interval: 0.94, 1.48, p = 0.15). Our study, therefore, did not provide evidence to support a potential causal relationship between telomere length and PD.

Smoking and amyotrophic lateral sclerosis: A mendelian randomization study.

In this study, we examined the potential causal effect of smoking on amyotrophic lateral sclerosis (ALS) using the Project MinE data involving 12,577 patients with ALS and 23,475 controls in a Mendelian randomization (MR) framework. The MR approach has the potential to investigate a causal relationship between a risk factor and a disease, avoiding confounding and information bias that often present in conventional epidemiological studies. We found that smokers had a higher risk of ALS compared to never smokers. Our study thus provides evidence for a causal relationship between smoking and ALS. Ann Neurol 2019;85:482-484.

Causal Inference in Cancer Epidemiology: What Is the Role of Mendelian Randomization?

Observational epidemiologic studies are prone to confounding, measurement error, and reverse causation, undermining robust causal inference. Mendelian randomization (MR) uses genetic variants to proxy modifiable exposures to generate more reliable estimates of the causal effects of these exposures on diseases and their outcomes. MR has seen widespread adoption within cardio-metabolic epidemiology, but also holds much promise for identifying possible interventions for cancer prevention and treatment. However, some methodologic challenges in the implementation of MR are particularly pertinent when applying this method to cancer etiology and prognosis, including reverse causation arising from disease latency and selection bias in studies of cancer progression. These issues must be carefully considered to ensure appropriate design, analysis, and interpretation of such studies. In this review, we provide an overview of the key principles and assumptions of MR, focusing on applications of this method to the study of cancer etiology and prognosis. We summarize recent studies in the cancer literature that have adopted a MR framework to highlight strengths of this approach compared with conventional epidemiological studies. Finally, limitations of MR and recent methodologic developments to address them are discussed, along with the translational opportunities they present to inform public health and clinical interventions in cancer. Cancer Epidemiol Biomarkers Prev; 27(9); 995-1010. ©2018 AACR.

Towards the elimination of paediatric tuberculosis in high-income, immigrant-receiving countries: a 25-year conventional and molecular epidemiological case study.

The epidemiology of tuberculosis (TB) in high-income countries is increasingly dictated by immigration. The influence of this trend on paediatric TB and TB elimination are not well defined.We undertook a 25-year conventional and molecular epidemiologic study of paediatric TB in Alberta, one of four major immigrant-receiving provinces in Canada. All isolates of Mycobacterium tuberculosis were DNA fingerprinted using standard methodology.Between 1990 and 2014, 176 children aged 0–14 years were diagnosed with TB. Foreign-born children or Canadian-born children of foreign-born parents accounted for an increasingly large proportion of total cases during the study period (from 32.1% to 89.5%). Of the 78 culture-positive cases, 35 (44.9%) had a putative source case identified by conventional epidemiology, with 34 (97.1%) having a concordant molecular profile. Of the remaining 43 culture-positive cases, molecular profiling identified spatially and temporally related sources in six cases (14.0%). These six children, along with four other children whose source cases were discovered through reverse-contact tracing, had a high morbidity and mortality.The increasing burden of paediatric TB in both foreign-born children and Canadian-born children of foreign-born parents calls for more timely diagnosis of source cases and more targeted screening for latent TB infection.

Association Between Thyroid-Stimulating Hormone and Renal Function: a Mendelian Randomization Study

Background/Aims: Increasing evidence suggests an association between thyroid-stimulating hormone (TSH) and estimated glomerular filtration rate (eGFR). We conducted a Mendelian randomization (MR) analysis to examine the causality of the association between TSH and eGFR. Methods: 10,603 participants were recruited from the Survey on Prevalence in East China for Metabolic Diseases and Risk Factors (SPECT-China), which was performed in 23 sites in East China during 2014-2016. We constructed weighted genetic risk scores (GRS) for TSH based on three TSH-related single nucleotide polymorphisms. eGFR was calculated using the CKD Epidemiology Collaboration formula. The instrumental variable (IV) was used to explore the causal relationship between TSH and eGFR. Results: Higher measured TSH levels were associated with lower eGFR (B -0.717, 95%CI -0.958, -0.476) after multivariable adjustment. However, by MR analysis, per SD increase in the TSH_GRS was significantly associated with TSH (B 0.155, 95%CI 0.076, 0.235, P< 0.001) but not with eGFR (B -0.127, 95%CI -0.364, 0.110). Using IV estimator, no causal associations were observed for genetically instrumented TSH with eGFR. Conclusion: By a genetic approach that limits residual confounding and reverse causation in observational conventional epidemiological studies, TSH and eGFR are not causally associated, which suggests genetically elevated TSH concentrations may not affect the renal function.

Genetic association of telomere length with hepatocellular carcinoma risk: A Mendelian randomization analysis

BackgroundObservational studies show an association between telomere length and Hepatocellular carcinoma (HCC) risk, but the relationship is controversial. Particularly, it remains unclear whether the association is due to confounding or biases inherent in conventional epidemiological studies. Here, we applied Mendelian randomization approach to evaluate whether telomere length is causally associated with HCC risk. MethodsIndividual-level data were from HBV-related HCC Genome-wide association studies (1,538 HBV positive HCC patients and 1,465 HBV positive controls). Genetic risk score, as proxy for actual measured telomere length, derived from nine telomere length-associated genetic variants was used to evaluate the effect of telomere length on HCC risk. ResultsWe observed a significant risk signal between genetically increased telomere length and HBV-related HCC risk (OR=2.09, 95% CI 1.32–3.31, P=0.002). Furthermore, a U-shaped curve was fitted by the restricted cubic spline curve, which indicated that either short or long telomere length would increase HCC risk (P=0.0022 for non-linearity test). Subgroup analysis did not reveal significant heterogeneity between different age, gender, smoking status and drinking status groups. ConclusionsOur results indicated that a genetic background that favors longer or shorter telomere length may increase HBV-related HCC risk—a U-shaped association.

Dose-response analyses of uric acid and risk of type 2 diabetes in a Chinese occupational population: a cohort study and meta-analysis

BackgroundThe link between hyperuricaemia and risk of type 2 diabetes has long been hypothesised, although the debate about the causality of this is unresolved. To better understand the causal role of high concentrations of serum uric acid in the development of diabetes, we comprehensively assessed the association between serum uric acid concentration and the risk of incident diabetes in an occupational population of Chinese men and women. MethodsWe used Restricted Cubic Splines functions and Cox proportional hazards models to estimate the association between serum uric acid measured at baseline and the risk of incident diabetes in the Jinchang cohort study, an ongoing occupational-based prospective study of 42 122 metal-exposed workers aged 20 years or older at baseline (2011–13). Only those who participated in the baseline survey and first follow-up with repeat medical examinations were eligible. Serum uric acid was measured with the uricase-peroxidase enzymatic method. We also performed a meta-analysis of ongoing and completed studies in both the occupational and general populations to summarise this association. FindingsAmong 30 917 participants without diagnosed diabetes at baseline, there were 934 incident diabetes cases during a median of 2·2 years (IQR 1·8–2·5) of follow-up. We observed a positive linear dose–response association between serum uric acid concentration and incident diabetes by spline analyses (poverall association=0·001; pnon-linearity=0·90). After multiple adjustments such as age, BMI, and occupation, the hazard ratio (HR) for incident diabetes was 1·62 (95% CI 1·26–2·08) for the fourth and highest quartile concentration of serum uric acid (ptrend<0·0001), compared with the first quartile. Furthermore, when modelled as a continuous variable, serum uric acid concentration was associated with incident diabetes: the risk of diabetes was increased by 13% per 1 mg/dL increase in serum uric acid concentration (HR 1·13, 95% CI 1·07–1·19). Analysing men and women separately, the positive trends still remained in men (ptrend=0·01), but was not associated in women (ptrend=0·06). However, when modelled as a continuous variable, serum uric acid concentration was significantly associated with the risk of diabetes both in men and women. In a meta-analysis of 14 studies comprising a total of 137 485 participants and 9351 cases of type 2 diabetes, comparing the highest versus lowest quartile of serum uric acid concentration, the HR for incident diabetes was 1·44 (95% CI 1·34–1·55). InterpretationOur findings from the Jinchang cohort show a positive linear dose–response association between serum uric acid concentration and risk of incident type 2 diabetes in both Chinese men and women. These findings were substantiated in an updated meta-analysis, suggesting that high serum uric acid concentration might identify individuals at risk of diabetes. More studies, such as Mendelian randomisation studies and conventional epidemiological studies, are needed to establish causality. FundingProject of Science and Technology of the Jinchang Nonferrous Metals Corporation (Grant JKB20120013) and US National Institutes of Health (D43TW 008323, D43Tw 007864–01, and DK66401).