Immunological and cardiometabolic risk factors in the prediction of type 2 diabetes and coronary events: MONICA/KORA Augsburg case-cohort study

Background

This study compares inflammation-related biomarkers with established cardiometabolic risk factors in the prediction of incident type 2 diabetes and incident coronary events in a prospective case-cohort study within the population-based MONICA/KORA Augsburg cohort.

Methods and Findings

Analyses for type 2 diabetes are based on 436 individuals with and 1410 individuals without incident diabetes. Analyses for coronary events are based on 314 individuals with and 1659 individuals without incident coronary events. Mean follow-up times were almost 11 years. Areas under the receiver-operating characteristic curve (AUC), changes in Akaike''s information criterion (ΔAIC), integrated discrimination improvement (IDI) and net reclassification index (NRI) were calculated for different models. A basic model consisting of age, sex and survey predicted type 2 diabetes with an AUC of 0.690. Addition of 13 inflammation-related biomarkers (CRP, IL-6, IL-18, MIF, MCP-1/CCL2, IL-8/CXCL8, IP-10/CXCL10, adiponectin, leptin, RANTES/CCL5, TGF-β1, sE-selectin, sICAM-1; all measured in nonfasting serum) increased the AUC to 0.801, whereas addition of cardiometabolic risk factors (BMI, systolic blood pressure, ratio total/HDL-cholesterol, smoking, alcohol, physical activity, parental diabetes) increased the AUC to 0.803 (ΔAUC [95% CI] 0.111 [0.092–0.149] and 0.113 [0.093–0.149], respectively, compared to the basic model). The combination of all inflammation-related biomarkers and cardiometabolic risk factors yielded a further increase in AUC to 0.847 (ΔAUC [95% CI] 0.044 [0.028–0.066] compared to the cardiometabolic risk model). Corresponding AUCs for incident coronary events were 0.807, 0.825 (ΔAUC [95% CI] 0.018 [0.013–0.038] compared to the basic model), 0.845 (ΔAUC [95% CI] 0.038 [0.028–0.059] compared to the basic model) and 0.851 (ΔAUC [95% CI] 0.006 [0.003–0.021] compared to the cardiometabolic risk model), respectively.

Conclusions

Inclusion of multiple inflammation-related biomarkers into a basic model and into a model including cardiometabolic risk factors significantly improved the prediction of type 2 diabetes and coronary events, although the improvement was less pronounced for the latter endpoint.     

Both Fasting and Glucose-Stimulated Proinsulin Levels Predict Hyperglycemia and Incident Type 2 Diabetes: A Population-Based Study of 9,396 Finnish Men

Background

Hyperproinsulinemia is an indicator of β-cell dysfunction, and fasting proinsulin levels are elevated in patients with hyperglycemia. It is not known whether proinsulin levels after a glucose load are better predictors of hyperglycemia and type 2 diabetes than fasting proinsulin.

Methods

Participants were 9,396 Finnish men (mean±SD, age 57.3±7.1 years, BMI 27.0±4.0 kg/m²) of the population-based METabolic Syndrome In Men Study who were non-diabetic at the recruitment, and who participated in a 6-year follow-up study. Proinsulin and insulin levels were measured in the fasting state and 30 and 120 min after an oral glucose load. Area under the curve (AUC) and proinsulin to insulin ratios were calculated.

Results

Fasting proinsulin, proinsulin at 30 min and proinsulin AUC during the first 30 min of an oral glucose tolerance test significantly predicted both the worsening of hyperglycemia and type 2 diabetes after adjustment for confounding factors. Further adjustment for insulin sensitivity (Matsuda index) or insulin secretion (Disposition index) weakened these associations. Insulin sensitivity had a major impact on these associations.

Conclusion

Our results suggest that proinsulin in the fasting state and after an oral glucose load similarly predict the worsening of hyperglycemia and conversion to type 2 diabetes.     

Do Variants Associated with Susceptibility to Pancreatic Cancer and Type 2 Diabetes Reciprocally Affect Risk?

ObjectivesAlthough type 2 diabetes mellitus is a known risk factor for pancreatic cancer, the existence of shared genetic susceptibility is largely unknown. We evaluated whether any reported genetic risk variants of either disease found by genome-wide association studies reciprocally confer susceptibility.MethodsData that were generated in previous genome-wide association studies (GENEVA Type 2 Diabetes; PanScan) were obtained through the National Institutes of Health database of Genotypes and Phenotypes (dbGaP). Using the PanScan datasets, we tested for association of 38 variants within 37 genomic regions known to be susceptibility factors for type 2 diabetes. We further examined whether type 2 diabetes variants predispose to pancreatic cancer risk stratified by diabetes status. Correspondingly, we examined the association of fourteen pancreatic cancer susceptibility variants within eight genomic regions in the GENEVA Type 2 Diabetes dataset.ResultsFour plausible associations of diabetes variants and pancreatic cancer risk were detected at a significance threshold of p = 0.05, and one pancreatic cancer susceptibility variant was associated with diabetes risk at threshold of p = 0.05, but none remained significant after correction for multiple comparisons.ConclusionCurrently identified GWAS susceptibility variants are unlikely to explain the potential shared genetic etiology between Type 2 diabetes and pancreatic cancer.     

Studies of the association of Arg72Pro of tumor suppressor protein p53 with type 2 diabetes in a combined analysis of 55,521 Europeans

Aims

A study of 222 candidate genes in type 2 diabetes reported association of variants in RAPGEF1, ENPP1, TP53, NRF1, SLC2A2, SLC2A4 and FOXC2 with type 2 diabetes in 4,805 Finnish individuals. We aimed to replicate these associations in a Danish case-control study and to substantiate any replicated associations in meta-analyses. Furthermore, we evaluated the impact on diabetes-related intermediate traits in a population-based sample of middle-aged Danes.

Methods

We genotyped nine lead variants in the seven genes in 4,973 glucose-tolerant and 3,612 type 2 diabetes Danish individuals. In meta-analyses we combined case-control data from the DIAGRAM+ Consortium (n = 47,117) and the present genotyping results. The quantitative trait studies involved 5,882 treatment-naive individuals from the Danish Inter99 study.

Results

None of the nine investigated variants were significantly associated with type 2 diabetes in the Danish samples. However, for all nine variants the estimate of increase in type 2 diabetes risk was observed for the same allele as previously reported. In a meta-analysis of published and online data including 55,521 Europeans the G-allele of rs1042522 in TP53 showed significant association with type 2 diabetes (OR = 1.06 95% CI 1.02–1.11, p = 0.0032). No substantial associations with diabetes-related intermediary phenotypes were found.

Conclusion

The G-allele of TP53 rs1042522 is associated with an increased prevalence of type 2 diabetes in a combined analysis of 55,521 Europeans.     

Plasma protein N-glycan signatures of type 2 diabetes

Background

Little is known about enzymatic N-glycosylation in type 2 diabetes, a common posttranslational modification of proteins influencing their function and integrating genetic and environmental influences. We sought to gain insights into N-glycosylation to uncover yet unexplored pathophysiological mechanisms in type 2 diabetes.

Dynamic alterations in serum IgG N-glycan profiles in the development of colitis-associated colon Cancer in mouse model

BackgroundAlternative glycosylation of serum IgG has been shown to be closely associated with colorectal cancer (CRC). Currently, a dynamic study which can not only minimize the influence of genetic background, environment and other interfering factors during cancer development, but also focus on investigating carcinogenic characteristics of IgG glycan is lacking.MethodsSerum IgG N-glycans were characterized at four stages of CRC development by ultra-performance liquid chromatography in a typical colitis-related CRC mouse model induced by azoxymethane-dextran sodium sulfate. Furthermore, the expression of related glycosyltransferases in splenic B lymphocytes at the corresponding time was also assessed.ResultsThe relative abundance of seven IgG glycans, which can be classified as monoantennary, core fucose, sialic acid, galactose and bisecting, was changed during tumor growth. The abundance of some glycans was altered during the first stage of cancer induction. Correspondingly, the expression of glycosyltransferases in splenic B lymphocytes and different tissues in cancer groups was also decreased compared to that in controls.ConclusionsThis study represents the comprehensive analysis of IgG glycosylation in the dynamic process of colitis-associated CRC. To our knowledge, this is the first report that the expression of glycosyltransferases in mouse splenic B lymphocytes is consistent or inconsistent with the alterations of IgG N-glycans, and the variation tendency is tissue nonspecific.General SignificanceProviding a novel approach to identify the IgG glycans related to the development of CRC and laying a foundation for research on structure and function of glycans using mouse.     

Associations of Circulating Lymphocyte Subpopulations with Type 2 Diabetes: Cross-Sectional Results from the Multi-Ethnic Study of Atherosclerosis (MESA)

ObjectiveDistinct lymphocyte subpopulations have been implicated in the regulation of glucose homeostasis and obesity-associated inflammation in mouse models of insulin resistance. Information on the relationships of lymphocyte subpopulations with type 2 diabetes remain limited in human population-based cohort studies.MethodsCirculating levels of innate (γδ T, natural killer (NK)) and adaptive immune (CD4⁺ naive, CD4⁺ memory, Th1, and Th2) lymphocyte subpopulations were measured by flow cytometry in the peripheral blood of 929 free-living participants of the Multi-Ethnic Study of Atherosclerosis (MESA). Cross-sectional relationships of lymphocyte subpopulations with type 2 diabetes (n = 154) and fasting glucose and insulin concentrations were evaluated by generalized linear models.ResultsEach standard deviation (SD) higher CD4⁺ memory cells was associated with a 21% higher odds of type 2 diabetes (95% CI: 1–47%) and each SD higher naive cells was associated with a 22% lower odds (95% CI: 4–36%) (adjusted for age, gender, race/ethnicity, and BMI). Among participants not using diabetes medication, higher memory and lower naive CD4⁺ cells were associated with higher fasting glucose concentrations (p<0.05, adjusted for age, sex, and race/ethnicity). There were no associations of γδ T, NK, Th1, or Th2 cells with type 2 diabetes, glucose, or insulin.ConclusionsA higher degree of chronic adaptive immune activation, reflected by higher memory and lower naive CD4⁺ cells, was positively associated with type 2 diabetes. These results are consistent with a role of chronic immune activation and exhaustion augmenting chronic inflammatory diseases, and support the importance of prospective studies evaluating adaptive immune activation and type 2 diabetes.     

Polygenic scores,diet quality,and type 2 diabetes risk: An observational study among 35,759 adults from 3 US cohorts

BackgroundBoth genetic and lifestyle factors contribute to the risk of type 2 diabetes, but the extent to which there is a synergistic effect of the 2 factors is unclear. The aim of this study was to examine the joint associations of genetic risk and diet quality with incident type 2 diabetes.Methods and findingsWe analyzed data from 35,759 men and women in the United States participating in the Nurses’ Health Study (NHS) I (1986 to 2016) and II (1991 to 2017) and the Health Professionals Follow-up Study (HPFS; 1986 to 2016) with available genetic data and who did not have diabetes, cardiovascular disease, or cancer at baseline. Genetic risk was characterized using both a global polygenic score capturing overall genetic risk and pathway-specific polygenic scores denoting distinct pathophysiological mechanisms. Diet quality was assessed using the Alternate Healthy Eating Index (AHEI). Cox models were used to calculate hazard ratios (HRs) for type 2 diabetes after adjusting for potential confounders. With over 902,386 person-years of follow-up, 4,433 participants were diagnosed with type 2 diabetes. The relative risk of type 2 diabetes was 1.29 (95% confidence interval [CI] 1.25, 1.32; P < 0.001) per standard deviation (SD) increase in global polygenic score and 1.13 (1.09, 1.17; P < 0.001) per 10-unit decrease in AHEI. Irrespective of genetic risk, low diet quality, as compared to high diet quality, was associated with approximately 30% increased risk of type 2 diabetes (P_interaction = 0.69). The joint association of low diet quality and increased genetic risk was similar to the sum of the risk associated with each factor alone (P_interaction = 0.30). Limitations of this study include the self-report of diet information and possible bias resulting from inclusion of highly educated participants with available genetic data.ConclusionsThese data provide evidence for the independent associations of genetic risk and diet quality with incident type 2 diabetes and suggest that a healthy diet is associated with lower diabetes risk across all levels of genetic risk.

In an observational study of 3 cohorts in the United States, Jordi Merino, Marta Guasch-Ferré, Jun Li, and colleagues investigate the individual and combined associations between genetic risk, diet quality, and risk of type 2 diabetes.     

Phenome-Wide Association Study (PheWAS) for Detection of Pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network

Using a phenome-wide association study (PheWAS) approach, we comprehensively tested genetic variants for association with phenotypes available for 70,061 study participants in the Population Architecture using Genomics and Epidemiology (PAGE) network. Our aim was to better characterize the genetic architecture of complex traits and identify novel pleiotropic relationships. This PheWAS drew on five population-based studies representing four major racial/ethnic groups (European Americans (EA), African Americans (AA), Hispanics/Mexican-Americans, and Asian/Pacific Islanders) in PAGE, each site with measurements for multiple traits, associated laboratory measures, and intermediate biomarkers. A total of 83 single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) were genotyped across two or more PAGE study sites. Comprehensive tests of association, stratified by race/ethnicity, were performed, encompassing 4,706 phenotypes mapped to 105 phenotype-classes, and association results were compared across study sites. A total of 111 PheWAS results had significant associations for two or more PAGE study sites with consistent direction of effect with a significance threshold of p<0.01 for the same racial/ethnic group, SNP, and phenotype-class. Among results identified for SNPs previously associated with phenotypes such as lipid traits, type 2 diabetes, and body mass index, 52 replicated previously published genotype–phenotype associations, 26 represented phenotypes closely related to previously known genotype–phenotype associations, and 33 represented potentially novel genotype–phenotype associations with pleiotropic effects. The majority of the potentially novel results were for single PheWAS phenotype-classes, for example, for CDKN2A/B rs1333049 (previously associated with type 2 diabetes in EA) a PheWAS association was identified for hemoglobin levels in AA. Of note, however, GALNT2 rs2144300 (previously associated with high-density lipoprotein cholesterol levels in EA) had multiple potentially novel PheWAS associations, with hypertension related phenotypes in AA and with serum calcium levels and coronary artery disease phenotypes in EA. PheWAS identifies associations for hypothesis generation and exploration of the genetic architecture of complex traits.     

Serum protein N-glycosylation changes in multiple myeloma

BackgroundMultiple myeloma is characterized by clonal proliferation of malignant plasma cells in the bone marrow that produce monoclonal immunoglobulins. N-glycosylation changes of these monoclonal immunoglobulins have been reported in multiple myeloma, but previous studies only detected limited serum N-glycan features.MethodsHere, a more detailed study of the human serum N-glycome of 91 multiple myeloma patients and 51 controls was performed. We additionally analyzed sequential samples from patients (n = 7) which were obtained at different time points during disease development as well as 16 paired blood serum and bone marrow plasma samples. N-glycans were enzymatically released and measured by mass spectrometry after linkage specific derivatization of sialic acids.ResultsA decrease in both α2,3- and α2,6-sialylation, galactosylation and an increase in fucosylation within complex-type N-glycans were found in multiple myeloma patients compared to controls, as well as a decrease in difucosylation of diantennary glycans. The observed glycosylation changes were present in all ISS stages, including the “low-risk” ISS I. In individual patients, difucosylation of diantennary glycans decreased with development of the disease. Protein N-glycosylation features from blood and bone marrow showed strong correlation. Moreover, associations of monoclonal immunoglobulin (M-protein) and albumin levels with glycan traits were discovered in multiple myeloma patients.Conclusions & general significanceIn conclusion, serum protein N-glycosylation analysis could successfully distinguish multiple myeloma from healthy controls. Further studies are needed to assess the potential roles of glycan trait changes and the associations of glycans with clinical parameters in multiple myeloma early detection and prognosis.     

Association Between Non-Alcoholic Fatty Liver Disease and Diabetes-Related Microvascular Complications: A Retrospective Cross-Sectional Study of Hospitalized Patients

ObjectiveOwing to limited research, the effect of nonalcoholic fatty liver disease (NAFLD) on type 2 diabetes outcomes remains unclear. This study aimed to investigate the association between NAFLD and microvascular complications in hospitalized patients with type 2 diabetes.MethodsWe included 1982 patients with type 2 diabetes. NAFLD was defined as hepatic steatosis detected by ultrasound without secondary causes of fat accumulation. The diagnosis of diabetic retinopathy (DR), diabetic kidney disease (DKD), and diabetic neuropathy was based on clinical medical records. Risk for advanced liver fibrosis was categorized as “low risk,” “indeterminate risk,” and “high risk,” based on the NAFLD fibrosis score (NAFLD-FS). Logistic regression was used to test the association between NAFLD, risk for advanced fibrosis, and the presence of DR, DKD, and diabetic neuropathy.ResultsThe prevalence of NAFLD was 61.3%. The presence of DR and DKD was inversely associated with NAFLD, after adjusting for covariates. The presence of DR and DKD was higher in the “indeterminate risk” and “high risk” groups than in the “low risk” group, after adjusting for the same covariates. Only the presence of DKD significantly increased with high NAFLD-FS.ConclusionThe presence of DR and DKD was inversely associated with NAFLD among hospitalized patients with type 2 diabetes. DKD was closely associated with high NAFLD-FS among patients with NAFLD.     

复杂疾病全基因组关联研究进展——遗传统计分析

2005年, Science杂志首次报道了有关人类年龄相关性黄斑变性的全基因组关联研究, 此后有关肥胖、2型糖尿病、冠心病、阿尔茨海默病等一系列复杂疾病的全基因组关联研究被陆续报道, 这一阶段被称为人类全基因组关联研究的第一次浪潮。文章分别介绍了全基因组关联研究统计分析的方法、软件和应用实例; 比较了关联分析中多重检验的P值调整方法, 包括Bonferroni、递减的Bonferroni校正法、模拟运算法和控制错误发现率的方法; 还讨论了人群混杂对关联分析结果可能产生的影响及原理, 以及全基因组关联研究中控制人群混杂的方法的研究进展和应用实例。在全基因组关联研究的第一次浪潮中, 应用经典的遗传统计方法发现了许多基因-表型之间的关联并且能够对这些关联做出解释, 其中包括许多基因组中的未知基因和染色体区域。然而, 全基因组关联研究的继续发展需要进一步阐述基因组内基因之间相互作用、基因-基因之间的复杂作用网络与环境因素的相互作用在复杂疾病发生中的作用, 现有的统计分析方法肯定不能满足需要, 开发更为高级的统计分析方法势在必行。最后, 文章还给出了全基因组关联研究统计分析软件的相关网站信息。     

Variants in GLIS3 and CRY2 are associated with type 2 diabetes and impaired fasting glucose in Chinese Hans

Background

Recent genome-wide association studies have identified a number of common variants associated with fasting glucose homeostasis and type 2 diabetes in populations of European origin. This is a replication study to examine whether such associations are also observed in Chinese Hans.

Methods

We genotyped nine variants in or near MADD, ADRA2A, CRY2, GLIS3, PROX1, FADS1, C2CD4B, IGF1 and IRS1 in a population-based cohort including 3,210 unrelated Chinese Hans from Beijing and Shanghai.

Results

We confirmed the associations of GLIS3-rs7034200 with fasting glucose (beta = 0.07 mmol/l, P = 0.03), beta cell function (HOMA-B) (beta = −3.03%, P = 0.009), and type 2 diabetes (OR [95%CI]  = 1.27 [1.09–1.49], P = 0.003) after adjustment for age, sex, region and BMI. The association for type 2 diabetes remained significant after adjusting for other diabetes related risk factors including family history of diabetes, lipid profile, medication information, hypertension and life style factors, while further adjustment for HOMA-B abolished the association. The A-allele of CRY2-rs11605924 was moderately associated with increased risk of combined IFG/type 2 diabetes (OR [95%CI]  = 1.15[1.01–1.30], P = 0.04). SNPs in or near MADD, ADRA2A, PROX1, FADS1, C2CD4B, IGF1, and IRS1 did not exhibit significant associations with type 2 diabetes or related glycemic traits (P≥0.10).

Conclusions

In conclusion, our results indicate the associations of GLIS3 locus with type 2 diabetes and impaired fasting glucose in Chinese Hans, partially mediated through impaired beta-cell function. In addition, we also found modest evidence for the association of CRY2-rs11605924 with combined IFG/type 2 diabetes.     

血清铁蛋白（SF）及sod水平与2型糖尿病患者小纤维神经病变关系的研究

摘要 目的：探血清铁蛋白（SF）及超氧化物歧化酶（sod）水平与2型糖尿病患者小纤维神经病变的关系。方法：选择2017年6月至2019年12月我院接诊的120例2型糖尿病患者,根据病变发生情况分为病变组67例,未发生病变53例作为对照组,分析血清铁蛋白（SF）及sod在其中的表达及其预测小纤维神经病变的价值。结果：病变组铁蛋白（SF）水平显著高于对照组,sod水平显著低于对照组,差异显著（P＜0.05）;ROC结果显示,铁蛋白（SF）预测2型糖尿病患者小纤维神经病变的AUC为0.924,95%CI为0.892~0.957,截断值为223.407 ng/mL ;sod预测2型糖尿病患者小纤维神经病变的AUC为0.96,95%CI为0.944~0.987,截断值为126.862 U/mL;联合预测2型糖尿病患者小纤维神经病变的AUC为0.993,95%CI为0.986~1.000,单独检测分别和联合检测曲线下面积比较均具有显著差异,联合检测的特异度、准确度分别为94.26%、95.16%。结论：在2型糖尿病小纤维神经病变患者中血清铁蛋白（SF）及sod的表达异常,对于疾病的进展有诊断意义,临床应给与关注。     

Sex-specific associations of adiposity with cardiometabolic traits in the UK: A multi–life stage cohort study with repeat metabolomics

BackgroundSex differences in cardiometabolic disease risk are commonly observed across the life course but are poorly understood and may be due to different associations of adiposity with cardiometabolic risk in females and males. We examined whether adiposity is differently associated with cardiometabolic trait levels in females and males at 3 different life stages.Methods and findingsData were from 2 generations (offspring, Generation 1 [G1] born in 1991/1992 and their parents, Generation 0 [G0]) of a United Kingdom population-based birth cohort study, the Avon Longitudinal Study of Parents and Children (ALSPAC). Follow-up continues on the cohort; data up to 25 y after recruitment to the study are included in this analysis. Body mass index (BMI) and total fat mass from dual-energy X-ray absorptiometry (DXA) were measured at mean age 9 y, 15 y, and 18 y in G1. Waist circumference was measured at 9 y and 15 y in G1. Concentrations of 148 cardiometabolic traits quantified using nuclear magnetic resonance spectroscopy were measured at 15 y, 18 y, and 25 y in G1. In G0, all 3 adiposity measures and the same 148 traits were available at 50 y. Using linear regression models, sex-specific associations of adiposity measures at each time point (9 y, 15 y, and 18 y) with cardiometabolic traits 3 to 6 y later were examined in G1. In G0, sex-specific associations of adiposity measures and cardiometabolic traits were examined cross-sectionally at 50 y. A total of 3,081 G1 and 4,887 G0 participants contributed to analyses. BMI was more strongly associated with key atherogenic traits in males compared with females at younger ages (15 y to 25 y), and associations were more similar between the sexes or stronger in females at 50 y, particularly for apolipoprotein B–containing lipoprotein particles and lipid concentrations. For example, a 1 standard deviation (SD) (3.8 kg/m²) higher BMI at 18 y was associated with 0.36 SD (95% confidence interval [CI] = 0.20, 0.52) higher concentrations of extremely large very-low-density lipoprotein (VLDL) particles at 25 y in males compared with 0.15 SD (95% CI = 0.09, 0.21) in females, P value for sex difference = 0.02. By contrast, at 50 y, a 1 SD (4.8 kg/m²) higher BMI was associated with 0.33 SD (95% CI = 0.25, 0.42) and 0.30 SD (95% CI = 0.26, 0.33) higher concentrations of extremely large VLDL particles in males and females, respectively, P value for sex difference = 0.42. Sex-specific associations of DXA-measured fat mass and waist circumference with cardiometabolic traits were similar to findings for BMI and cardiometabolic traits at each age. The main limitation of this work is its observational nature, and replication in independent cohorts using methods that can infer causality is required.ConclusionsThe results of this study suggest that associations of adiposity with adverse cardiometabolic risk begin earlier in the life course among males compared with females and are stronger until midlife, particularly for key atherogenic lipids. Adolescent and young adult males may therefore be high priority targets for obesity prevention efforts.

Linda O’Keeffe and colleagues investigate sex-specific associations between cardiometabolomic traits and BMI, fat mass, and waist circumference across childhood, adolescence, early adulthood, and mid life in two generations of a UK population-based birth cohort.     

A data mining based clinical decision support system for survival in lung cancer

BackgroundA clinical decision support system (CDSS ) has been designed to predict the outcome (overall survival) by extracting and analyzing information from routine clinical activity as a complement to clinical guidelines in lung cancer patients.Materials and methodsProspective multicenter data from 543 consecutive (2013–2017) lung cancer patients with 1167 variables were used for development of the CDSS. Data Mining analyses were based on the XGBoost and Generalized Linear Models algorithms. The predictions from guidelines and the CDSS proposed were compared.ResultsOverall, the highest (> 0.90) areas under the receiver-operating characteristics curve AUCs for predicting survival were obtained for small cell lung cancer patients. The AUCs for predicting survival using basic items included in the guidelines were mostly below 0.70 while those obtained using the CDSS were mostly above 0.70. The vast majority of comparisons between the guideline and CDSS AUCs were statistically significant (p < 0.05). For instance, using the guidelines, the AUC for predicting survival was 0.60 while the predictive power of the CDSS enhanced the AUC up to 0.84 (p = 0.0009). In terms of histology, there was only a statistically significant difference when comparing the AUCs of small cell lung cancer patients (0.96) and all lung cancer patients with longer (≥ 18 months) follow up (0.80; p < 0.001).ConclusionsThe CDSS successfully showed potential for enhancing prediction of survival. The CDSS could assist physicians in formulating evidence-based management advice in patients with lung cancer, guiding an individualized discussion according to prognosis.     

Which Obesity Index Best Explains Prevalence Differences in Type 2 Diabetes Mellitus?

Objective: Obesity drives the diabetes epidemic. However, it is not known which obesity index best explains variations in type 2 diabetes mellitus prevalence across populations. Research Methods and Procedures: We analyzed three cross‐sectional studies from San Antonio, TX, (Mexican‐Americans and non‐Hispanic whites, n = 2839), Mexico City (n = 2233), and Spain (n = 2161) (age range, 35 to 64 years). We used the area under the receiver operating characteristic curve (AUC) to assess performance for identifying diabetic subjects and logistic regression analysis to examine differences in diabetes prevalence. Results: AUCs for waist circumference and BMI were similar in white subjects, but the AUC for waist circumference was greater in Mexican‐origin subjects (Mexican men, 0.594 vs. 0.549, p = 0.008; and women, 0.605 vs. 0.557, p = 0.002; Mexican‐American men, 0.648 vs. 0.600, p < 0.001; and women, 0.744 vs. 0.693, p < 0.001). The AUC for waist‐to‐height ratio tended to be greater than that for waist circumference, but statistical significance was demonstrated only in Mexican women (0.628 vs. 0.613, p = 0.044), Mexican‐American women (0.774 vs. 0.758, p < 0.001), and Spanish women (0.734 vs. 0.715, p = 0.039). No obesity index was consistently superior to the others for explaining differences in diabetes prevalence among populations. Conclusions: In white and Mexican‐origin men, waist circumference may be the preferred marker for identifying diabetic subjects on account of its simplicity; in women, waist‐to‐height ratio may be better. Differences in diabetes prevalence among these populations cannot be attributed to a single measure of obesity.     

Effects of 16 genetic variants on fasting glucose and type 2 diabetes in South Asians: ADCY5 and GLIS3 variants may predispose to type 2 diabetes

BackgroundThe Meta-Analysis of Glucose and Insulin related traits Consortium (MAGIC) recently identified 16 loci robustly associated with fasting glucose, some of which were also associated with type 2 diabetes. The purpose of our study was to explore the role of these variants in South Asian populations of Punjabi ancestry, originating predominantly from the District of Mirpur, Pakistan.Conclusions/SignificanceAlthough only the SLC30A8 rs11558471 SNP was nominally associated with fasting glucose in our study, the finding that 12 out of 16 SNPs displayed a direction of effect consistent with European studies suggests that a number of these variants may contribute to fasting glucose variation in individuals of South Asian ancestry. We also provide evidence for the first time in South Asians that alleles of SNPs in GLIS3 and ADCY5 may confer risk of type 2 diabetes.     

Comparative Performance of Four Methods for High-throughput Glycosylation Analysis of Immunoglobulin G in Genetic and Epidemiological Research

The biological and clinical relevance of glycosylation is becoming increasingly recognized, leading to a growing interest in large-scale clinical and population-based studies. In the past few years, several methods for high-throughput analysis of glycans have been developed, but thorough validation and standardization of these methods is required before significant resources are invested in large-scale studies. In this study, we compared liquid chromatography, capillary gel electrophoresis, and two MS methods for quantitative profiling of N-glycosylation of IgG in the same data set of 1201 individuals. To evaluate the accuracy of the four methods we then performed analysis of association with genetic polymorphisms and age. Chromatographic methods with either fluorescent or MS-detection yielded slightly stronger associations than MS-only and multiplexed capillary gel electrophoresis, but at the expense of lower levels of throughput. Advantages and disadvantages of each method were identified, which should inform the selection of the most appropriate method in future studies.Glycans are important structural and functional components of the majority of proteins, but because of their structural complexity and the absence of a direct genetic template our current understanding of the role of glycans in biological processes lags significantly behind the knowledge about proteins or DNA (1, 2). However, a recent comprehensive report endorsed by the US National Academies concluded that “glycans are directly involved in the pathophysiology of every major disease and that additional knowledge from glycoscience will be needed to realize the goals of personalized medicine” (3).It is estimated that the glycome (defined as the complete set of all glycans) of a eukaryotic cell is composed of more than a million different glycosylated structures (1), which contain up to 10,000 structural glycan epitopes for interaction with antibodies, lectins, receptors, toxins, microbial adhesins, or enzymes (4). Our recent population-based studies indicated that the composition of the human plasma N-glycome varies significantly between individuals (5, 6). Because glycans have important structural and regulatory functions on numerous glycoproteins (7), the observed variability suggests that differences in glycosylation might contribute to a large part of the human phenotypic variability. Interestingly, when the N-glycome of isolated immunoglobulin G (IgG)¹ was analyzed, it was found to be even more variable than the total plasma N-glycome (8), indicating that the combined analysis of all plasma glycans released from many different glycoproteins blurs signals of protein-specific regulation of glycosylation.A number of studies have investigated the role of glycans in human disease, including autoimmune diseases and cancer (9, 10). However, most human glycan studies have been conducted with very small sample sizes. Given the complex causal pathways involved in pathophysiology of common complex disease, and thus the likely modest effect sizes associated with individual factors, the majority of these studies are very likely to be substantially underpowered. In the case of inflammatory bowel disease, only 20% of reported inflammatory bowel disease glycan associations were replicated in subsequent studies, suggesting that most are false positive findings and that there is publication bias favoring the publication of positive findings (11). This situation is similar to that which occurred in the field of genetic epidemiology in the past when many underpowered candidate gene studies were published and were later found to consist of mainly false positive findings (12, 13). It is essential, therefore, that robust and affordable methods for high-throughput analysis are developed so that adequately powered studies can be conducted and the publication of large numbers of small studies reporting false positive results (which could threaten the credibility of glycoscience) be avoided.Rapid advances of technologies for high-throughput genome analysis in the past decade enabled large-scale genome-wide association studies (GWAS). GWAS has become a reliable tool for identification of associations between genetic polymorphisms and various human diseases and traits (14). Thousands of GWAS have been conducted in recent years, but these have not included the study of glycan traits until recently. The main reason was the absence of reliable tools for high-throughput quantitative analysis of glycans that could match the measurements of genomic, biochemical, and other traits in their cost, precision, and reproducibility. However, several promising high-throughput technologies for analysis of N-glycans were developed (8, 15–20) recently. Successful implementation of high-throughput analytical techniques for glycan analysis resulted in publication of four initial GWAS of the human glycome (21–24).In this study, we compared ultra-performance liquid chromatography with fluorescence detection (UPLC-FLR), multiplex capillary gel electrophoresis with laser induced fluorescence detection (xCGE-LIF), matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), and liquid chromatography electrospray mass spectrometry (LC-ESI-MS) as tools for mid-to-high-throughput glycomics and glycoproteomics. We have analyzed IgG N-glycans by all four methods in 1201 individuals from European populations. The analysis of associations between glycans and ∼300,000 single-nucleotide genetic polymorphisms was performed and correlation between glycans and age was studied in all four data sets to identify the analytical method that shows the strongest potential to uncover biological mechanisms underlying protein glycosylation.     

The Relationship between Nonalcoholic Fatty Liver Disease and Retinopathy in NHANES III

BackgroundNonalcoholic fatty liver disease (NAFLD), an emerging multisystem disease, has the similar pathogenesis with diabetes and is prevalent in diabetes. This study investigated whether NAFLD is associated with retinopathy in individuals with diabetes and without diabetes.MethodsThe association between NAFLD and retinopathy was investigated in 5963 participants aged 40 years and older who participated in the NHANES III, a nationally representative, population-based and cross-sectional study. NAFLD was detected via ultrasonography, and fundus photographs were obtained to grade retinopathy patterns. We performed multivariate logistic regression analysis to investigate the relationship between the presence of retinopathy and NAFLD and diabetes.ResultsAfter adjusting for multiple covariates, NAFLD population had no evidence of retinopathy increase in population without diabetes (odds ratio [OR]: 0.77; 95% confidence interval [CI]: 0.48 to 1.26). In addition, NAFLD in individuals with diabetes was not significantly associated with retinopathy (OR: 0.77; 95% CI: 0.47 to 1.26), independent of age, gender, ethnicity, waist circumference, serum high-density lipoprotein (HDL) cholesterol, serum triglycerides, systolic blood pressure, and glycated hemoglobin.ConclusionsIn the US general population, NAFLD is not a precipitating factor of retinopathy in population with or without diabetes.