Unravelling inulin molecules in food sources using a matrix-assisted laser desorption/ionization magnetic resonance mass spectrometry (MALDI-MRMS) pipeline.

Inulin, a polysaccharide characterized by a ß-2,1 fructosyl-fructose structure terminating in a glucosyl moiety, is naturally present in plant roots and tubers. Current methods provide average degrees of polymerization (DP) but lack information on the distribution and absolute concentration of each DP. To address this limitation, a reproducible (CV < 10 %) high throughput (<2 min/sample) MALDI-MRMS approach capable of characterizing and quantifying inulin molecules in plants using matched-matrix consisting of α-cyano-4-hydroxycinnamic acid butylamine salt (CHCA-BA), chicory inulin-12C and inulin-13C was developed. The method identified variation in chain lengths and concentration of inulin across various plant species. Globe artichoke hearts, yacón and elephant garlic yielded similar concentrations at 15.6 g/100 g dry weight (DW), 16.8 g/100 g DW and 17.7 g/100 g DW, respectively, for DP range between 9 and 22. In contrast, Jerusalem artichoke demonstrated the highest concentration (53.4 g/100 g DW) within the same DP ranges. Jerusalem artichoke (DPs 9-32) and globe artichoke (DPs 9-36) showed similar DP distributions, while yacón and elephant garlic displayed the narrowest and broadest DP ranges (DPs 9-19 and DPs 9-45, respectively). Additionally, qualitative measurement for all inulin across all plant samples was feasible using the peak intensities normalized to Inulin-13C, and showed that the ratio of yacón, elephant garlic and Jerusalem was approximately one, two and three times that of globe artichoke. This MALDI-MRMS approach provides comprehensive insights into the structure of inulin molecules, opening avenues for in-depth investigations into how DP and concentration of inulin influence gut health and the modulation of noncommunicable diseases, as well as shedding light on refining cultivation practices to elevate the beneficial health properties associated with specific DPs.

The Rapidly Expanding Nexus of Immunoglobulin G N-Glycomics, Suboptimal Health Status, and Precision Medicine.

Immunoglobulin G is a prevalent glycoprotein, whose downstream immune responses are partially mediated by the N-glycans within the fragment crystallisable domain. Collectively termed the N-glycome, it is considered a complex intermediate phenotype: an amalgamation of genetic predisposition, environmental exposure, and health behaviours over the life-course. Thus, the immunoglobulin G N-glycome may provide an indication of health status on the spectrum from health to disease and infirmary. Although variability exists within and between populations, composition of the immunoglobulin G N-glycome remains stable over short periods of time. This underscores the potential of harnessing the immunoglobulin G N-glycome as an ideal tool for preclinical disease risk prediction, stratification, and prognosis through the development of precise dynamic biomarkers.

N-glycosylation profiling of Type 2 diabetes mellitus from baseline to follow-up: an observational study in a Ghanaian population.

Aim: The study sought to determine the patterns of N-glycan profiles among Type 2 diabetes mellitus (T2DM) patients over a 6-month period. Materials & methods: Biochemical and clinical data were obtained from 253 T2DM patients at baseline and follow-up. Ultra-performance liquid chromatography and statistical methods were applied for N-glycan profiling. Results: The coefficients of variation were 28% and 29% at baseline and follow-up, respectively, whereas the range of N-glycan variability was from 11% to 56%. Apart from GP1 (FA2) and GP29 (FA3G3S [3,3,3]3), the intra-individual variations of N-glycan peaks were not statistically significant. Conclusion: N-glycan profiles were stable over 6-month period in T2DM patients and could be used to monitor biochemical changes in relation with T2DM comorbidities.

Type 2 Diabetes Mellitus is Associated with the Immunoglobulin G N-Glycome through Putative Proinflammatory Mechanisms in an Australian Population.

Type 2 diabetes mellitus (T2DM) is a common complex trait arising from interactions among multiple environmental, genomic, and postgenomic factors. We report here the first attempt to investigate the association between immunoglobulin G (IgG) N-glycan patterns, T2DM, and their clinical risk factors in an Australian population. N-glycosylation of proteins is one of the most frequently observed co- and post-translational modifications, reflecting, importantly, the real-time status of the interplay between the genomic and postgenomic factors. In a community-based case-control study, 849 participants (217 cases and 632 controls) were recruited from an urban community in Busselton, Western Australia. We applied the ultraperformance liquid chromatography method to analyze the composition of IgG N-glycans. We then conducted Spearman's correlation analyses to explore the association between glycan biomarker candidates and clinical risk factors. We performed area under the curve (AUC) analysis of the receiver operating characteristic curves by fivefold cross-validation for clinical risk factors, IgG glycans, and their combination. Two directly measured and four derived glycan peaks were significantly associated with T2DM, after correction for extensive clinical confounders and false discovery rate, thus suggesting that IgG N-glycan traits are highly correlated with T2DM clinical risk factors. Moreover, adding the IgG glycan profiles to fasting blood glucose in the logistic regression model increased the AUC from 0.799 to 0.859. The AUC for IgG glycans alone was 0.623 with a 95% confidence interval 0.580-0.666. In addition, our study provided new evidence of diversity in T2DM complex trait by IgG N-glycan stratification. Six IgG glycan traits were firmly associated with T2DM, which reflects an increased proinflammatory and biological aging status. In summary, our study reports novel associations between the IgG N-glycome and T2DM in an Australian population and the putative role of proinflammatory mechanisms. Furthermore, IgG N-glycomic alterations offer future prospects as inflammatory biomarker candidates for T2DM diagnosis, and monitoring of T2DM progression to cardiovascular disease or renal failure.

Utilization of N-glycosylation profiles as risk stratification biomarkers for suboptimal health status and metabolic syndrome in a Ghanaian population.

Aim: The study sought to apply N-glycosylation profiles to understand the interplay between suboptimal health status (SHS) and metabolic syndrome (MetS). Materials & methods: In this study, 262 Ghanaians were recruited from May to July 2016. After completing a health survey, plasma samples were collected for clinical assessments while ultra performance liquid chromatography was used to measure plasma N-glycans. Results: Four glycan peaks were found to predict case status (MetS and SHS) using a step-wise Akaike's information criterion logistic regression model selection. This model yielded an area under the curve of MetS: 83.1% (95% CI: 78.0-88.1%) and SHS: 67.1% (60.6-73.7%). Conclusion: Our results show that SHS is a significant, albeit modest, risk factor for MetS and N-glycan complexity was associated with MetS.

Increased central adiposity is associated with pro-inflammatory immunoglobulin G N-glycans.

BACKGROUND: Increased body fat may be associated with an increased risk of developing an underlying pro-inflammatory state, thus leading to greater risk of developing certain chronic conditions. Immunoglobulin G has the ability to exert both anti- and pro-inflammatory effects, and the N-glycosylation of the fragment crystallisable portion is involved in mediating this process. Body mass index, a rudimentary yet gold standard indication for body fat, has been shown to be associated with agalactosylated immunoglobulin G N-glycans. AIM: We aimed to determine the association between increased body fat and the immunoglobulin G glycosylation features, comparing body mass index to other measures of body fat distribution. METHODS: We investigated a sample of 637 community-based 45-69 year olds, with mixed phenotypes, residing in Busselton, Western Australia. Body mass index and the waist-to-hip and waist-to-height ratios were calculated using anthropometry, while dual-energy x-ray absorptiometry was performed to gain an accurate measure of total and area specific body fat. Serum immunoglobulin GN-glycans were analysed by ultra-performance liquid chromatography. RESULTS: Twenty-two N-glycan peaks were found to be associated with at least one of the fat measures. While the previous association of body mass index to agalactosylated immunoglobulin G was replicated, measures of central adiposity explained the most variation in the immunoglobulin G glycome. CONCLUSION: Central adiposity is associated with an increased pro-inflammatory fraction of immunoglobulin G, suggesting that the android/gynoid ratio or waist-to-height ratio instead be considered when controlling for adiposity in immunoglobulin G glycome biomarker studies.

High throughput profiling of whole plasma N-glycans in type II diabetes mellitus patients and healthy individuals: A perspective from a Ghanaian population.

Aberrant protein glycosylation may reflect changes in cell metabolism of type II diabetes mellitus (T2DM) and offers fresh vistas for discovering potential biomarkers. However, the functional significance of T2DM N-glycan alterations is underexplored, since to date, N-glycan profiling studies have been performed in selected populations. Geographically and genetically isolated populations are needed for validation of specific biomarkers. This age-sex matched cross sectional study comprising 232 T2DM patients and 219 controls was conducted in Ghana, Western Africa. Blood plasma samples were collected for clinical assessment after which plasma N-glycans were freed and analysed by ultra-performance liquid chromatography (UPLC). High branching (HB) [W = 46328; q = 0.00072], tri-galactosylated (G3) [W = 44076; q = 0.00096], antennary fucosylated (FUC_A) [W = 43055; q = 0.0000763], and triantennary (TRIA) [W = 44624; q = 0.0025], N-glycan structures were increased in T2DM whereas low branching (LB) [W = 46328; q = 0.00072], non-sialylated (S0) [W = 46929; q = 0.00292], monogalactosylation (G1) [W = 44091; q = 0.0000763], core fucosylation (FUC_C), [W = 46497; q = 0.00096], biantennary galactosylation (A2G) [W = 45663; q = 0.000763], and biantennary (BA) [W = 46376; q = 0.00072], structures were decreased compared to controls. Nine N-glycan peaks (GPs (GP1, GP4, GP7, GP11, GP17, GP19, GP22, GP26, GP29)) were found to predict case status based on Akaike's information criterion (AIC) and Bayesian information criterion (BIC) model selection. Adjusting for age, sex and other co-variates in this model yielded an area under the curve (AUC) of 80.5% with sensitivity of 79% and specificity of 73%, indicating the predicting power of N-glycans as robust biomarkers. Our results show that hyperglycemia influences N-glycan complexities among Ghanaians. N-glycan profiling in distinct populations has affirmed the potentiality of N-glycan profiles as generic biomarkers which may facilitate better prognosis for T2DM.

Impact of biobanks on research outcomes in rare diseases: a systematic review.

BACKGROUND: Alleviating the burden of rare diseases requires research into new diagnostic and therapeutic strategies. We undertook a systematic review to identify and compare the impact of stand-alone registries, registries with biobanks, and rare disease biobanks on research outcomes in rare diseases. METHODS: A systematic review and meta-aggregation was conducted using the preferred reporting items for systematic reviews and meta-analyses (the PRISMA statement). English language publications were sourced from PubMed, Medline, Scopus, and Web of Science. Original research papers that reported clinical, epidemiological, basic or translational research findings derived from data contained in stand-alone registries, registries with biobanks, and rare disease biobanks were considered. Articles selected for inclusion were assessed using the critical appraisal instruments by JBI-QARI. Each article was read in its entirety and findings were extracted using the online data extraction software from JBI-QARI. RESULTS: Thirty studies including 28 rare disease resources were included in the review. Of those, 14 registries were not associated to biobank infrastructure, 9 registries were associated with biobank infrastructure, and 6 were rare disease biobank resources. Stand-alone registries had the capacity to uncover the natural history of disease and contributed to evidence-based practice. When annexed to biobank infrastructure, registries could also identify and validate biomarkers, uncover novel genes, elucidate pathogenesis at the Omics level, and develop new therapeutic strategies. Rare disease biobanks in this review had similar capacity for biological investigations, but in addition, had far greater sample numbers and higher quality laboratory techniques for quality assurance processes. DISCUSSION: We examined the research outcomes of three specific populations: stand-alone registries, registries with biobanks, and stand-alone rare disease biobanks and demonstrated that there are key differences among these resources. These differences are a function of the resources' design, aims, and objectives, with each resource having a distinctive and important role in contributing to the body of knowledge for rare disease research. Whilst stand-alone registries had the capacity to uncover the natural history of disease, develop best practice, replace clinical trials, and improve patient outcomes, they were limited in their capacity to conduct basic research. The role of basic research in rare disease research is vital; scientists must first understand the pathways of disease before they can develop appropriate interventions. Rare disease biobanks, on the other hand (particularly larger biobanks), had the key infrastructure required to conduct basic research, making novel Omics discoveries, identify and validate biomarkers, uncover novel genes, and develop new therapeutic strategies. However, these stand-alone rare disease biobanks did not collect comprehensive data or impact on clinical observations like a rare disease registry. Rare disease research is important not only for rare diseases, but also for also common diseases. For example, research of low-density lipoprotein (LDL)-receptors in the rare disease known as familial hypercholesterolemia led to the discovery of statins, a drug therapy that is now used routinely to prevent heart disease. CONCLUSIONS: Rare diseases are still under-researched worldwide. This review made the important observation that registries with biobanks had the function of both stand-alone registries (the capacity to collect comprehensive clinical and epidemiological data) and stand-alone rare disease biobanks (the ability to contribute to Omics research). We found registries with biobanks offer a unique, practical, cost-effective, and impactful solution for rare disease research. Linkage of stand-alone registries to rare disease biobanks will provide the appropriate resources required for the effective translation of basic research into clinical practice. Furthermore, facilitators such as collaboration, engagement, blended recruitment, pro-active marketing, broad consent, and "virtual biobank" online catalogues will, if utilised, add to the success of these resources. These important observations can serve to direct future rare diseases research efforts, ultimately improve patient outcomes and alleviate the significant burden associated with rare disease for clinicians, hospitals, society, and most importantly, the patients and their families.

Unravelling Immunoglobulin G Fc N-Glycosylation: A Dynamic Marker Potentiating Predictive, Preventive and Personalised Medicine.

Multiple factors influence immunoglobulin G glycosylation, which in turn affect the glycoproteins' function on eliciting an anti-inflammatory or pro-inflammatory response. It is prudent to underscore these processes when considering the use of immunoglobulin G N-glycan moieties as an indication of disease presence, progress, or response to therapeutics. It has been demonstrated that the altered expression of genes that encode enzymes involved in the biosynthesis of immunoglobulin G N-glycans, receptors, or complement factors may significantly modify immunoglobulin G effector response, which is important for regulating the immune system. The immunoglobulin G N-glycome is highly heterogenous; however, it is considered an interphenotype of disease (a link between genetic predisposition and environmental exposure) and so has the potential to be used as a dynamic biomarker from the perspective of predictive, preventive, and personalised medicine. Undoubtedly, a deeper understanding of how the multiple factors interact with each other to alter immunoglobulin G glycosylation is crucial. Herein we review the current literature on immunoglobulin G glycoprotein structure, immunoglobulin G Fc glycosylation, associated receptors, and complement factors, the downstream effector functions, and the factors associated with the heterogeneity of immunoglobulin G glycosylation.

Innovation Analysis on Postgenomic Biomarkers: Glycomics for Chronic Diseases.

Despite decades of investment in biomarker research, we still do not have robust and field-tested biomarkers for many chronic diseases so as to anticipate clinical outcomes and thus move toward personalized medicine. Biomarker innovations have tended to focus on genomics, but next-generation biomarkers from the nascent field of glycomics now offer fresh vistas for innovation in chronic disease biomarkers and systems diagnostics. Glycosylation, regarded as a complex enzymatic process where sugars (glycans) bind to proteins and lipids, affects many human biological functions, including cell signaling, adhesion, and motility. Notably, and contrary to proteins, glycan biosynthesis does not require a template; rather its final structure is catalyzed by a repertoire of enzymes that attach or detach monosaccharides in the glycosylation pathway, making glycomics research more challenging than proteomics or genomics. Yet, given glycans' biological significance, alterations in their processing may be detrimental to human health and also offer insights for preventive medicine and wellness interventions. Therefore, studying glycans' structure and understanding their function and molecular interactions in the emerging field of glycomics are key to unraveling the pathogenesis of various common chronic diseases. This review summarizes the major concepts in glycomics, including glycan release methods, techniques for large-scale glycan analysis, and glycoinformatic tools for data handling and storage. In all, this analysis on glycomics offers strategies to build a robust postgenomic innovation roadmap for glycan-driven biomarkers as the field is anticipated to mature further and gain greater prominence in the near future.

The N-glycosylation of immunoglobulin G as a novel biomarker of Parkinson's disease.

The use of the emerging "omics" technologies for large scale population screening is promising in terms of predictive, preventive and personalized medicine. For Parkinson's disease, it is essential that an accurate diagnosis is obtained and disease progression can be monitored. Immunoglobulin G (IgG) has the ability to exert both anti-inflammatory and pro-inflammatory effects, and the N-glycosylation of the fragment crystallizable portion of IgG is involved in this process. This study aimed to determine whether the IgG glycome could be a candidate biomarker for Parkinson's disease. Ninety-four community-based individuals with Parkinson's disease and a sex-, age- and ethnically-matched cohort of 102 individuals with mixed phenotypes, representative of a "normally" aged Caucasian controls, were investigated. Plasma IgG glycans were analyzed by ultra-performance liquid chromatography. Overall, seven glycan peaks and 11 derived traits had statistically significant differences (P < 8.06 × 10-4) between Parkinson's disease cases and healthy controls. Out of the seven significantly different glycan peaks, four were selected by Akaike's Information Criterion to be included in the logistic regression model, with a sensitivity of 87.2% and a specificity of 92.2%. The study suggested that there may be a reduced capacity for the IgG to inhibit Fcγ-RIIIa binding, which would allow an increased ability for the IgG to cause antibody-dependent cell cytotoxicity and a possible state of low-grade inflammation in individuals with Parkinson's disease.

Association of Ideal Cardiovascular Health and Brachial-Ankle Pulse Wave Velocity: A Cross-Sectional Study in Northern China.

BACKGROUND: Brachial-ankle pulse wave velocity (baPWV) is an index for evaluating arterial stiffness and is recognized as an independent predictor of an impending cardiovascular event. Limited large-scale population data are available regarding the relationship between baPWV and the change in ideal cardiovascular health (CVH) status, particularly among Asians. METHODS: A random sample of 5199 participants (≥40 years of age; 40% women) was enrolled in a cross-sectional study in China to examine the association between ideal CVH and baPWV. Arterial stiffness was defined as a baPWV of 1400 cm/second or higher. Information on CVH was collected based on the American Heart Association's CVH definition by measuring all 7 components (nonsmoking status, body mass index, physical activity, healthy diet, normal total cholesterol, blood pressure, and fasting blood glucose), with 1 or 0 for each component, and a score from 0 to 7 for each participant. RESULTS: A significant and inverse association was found between the ideal CVH score and baPWV (P <.001). The adjusted odds ratios for arterial stiffness prevalence were .17 (95% confidence interval [CI], .11-.26), .26 (95% CI, .19-.35), .42 (95% CI, .32-.52), .54 (95% CI, .42-.69), and .69 (95% CI, .54-.89) for those with CVH scores of 7-6, 5, 4, 3, and 2, respectively, compared with those with a CVH score of 1-0 (P <.001). Similarly graded relationships were observed in different age and gender subgroups (P <.001). CONCLUSION: The favorable score of ideal CVH was inversely related to baPWV in Chinese adults, supporting the use of ideal CVH metrics as a useful tool for public health efforts.

The impact of dementia development concurrent with Parkinson's disease: a new perspective.

Dementia is the leading cause of disability worldwide among chronic diseases in the elderly and is a major contributor to mortality. Importantly, dementia that develops as a comorbid condition significantly compounds the burden of disease on the person, their caregivers and the health care system. Dementia is a frequent comorbidity of Parkinson's disease (PD) and about 80% of people with PD will develop dementia during the course of the disease. Incidence of dementia in PD ranges from 54.7 to 107.14 per 1000 person-years while point prevalence estimates range from 19.7 to 35.3%. The range in incidence and point prevalence can be attributed to varying diagnostic criteria, sample biases, and sample size. Nosologically, there is still disagreement on the origins of dementia in PD. Dementia development may be most often caused by the progression of PD-type pathology; however, the occurrence of Alzheimer's disease (AD)-type pathology suggests that an interplay exists between the genes and proteins associated with PD and AD. Furthermore, these genes and proteins may increase the risk and severity of dementia development in people with PD. Understanding the mechanisms of neurodegeneration in PD and AD may, therefore, improve efforts to manage and treat PD dementia. Given this, it is important to adequately define the frequency of PD dementia for informed decision making, particularly in the areas of aged-care and government health policy.

Traditional Chinese medicine and new concepts of predictive, preventive and personalized medicine in diagnosis and treatment of suboptimal health.

BACKGROUND: The premise of disease-related phenotypes is the definition of the counterpart normality in medical sciences. Contrary to clinical practices that can be carefully planned according to clinical needs, heterogeneity and uncontrollability is the essence of humans in carrying out health studies. Full characterization of consistent phenotypes that define the general population is the basis to individual difference normalization in personalized medicine. Self-claimed normal status may not represent health because asymptomatic subjects may carry chronic diseases at their early stage, such as cancer, diabetes mellitus and atherosclerosis. Currently, treatments for non-communicable chronic diseases (NCD) are implemented after disease onset, which is a very much delayed approach from the perspective of predictive, preventive and personalized medicine (PPPM). A NCD pandemic will develop and be accompanied by increased global economic burden for healthcare systems throughout both developed and developing countries. This paper examples the characterization of the suboptimal health status (SHS) which represents a new PPPM challenge in a population with ambiguous health complaints such as general weakness, unexplained medical syndrome (UMS), chronic fatigue syndrome (CFS), myalgic encephalomyelitis (ME), post-viral fatigue syndrome (PVFS) and chronic fatigue immune dysfunction syndrome (CFIDS). METHODS: We applied clinical informatic approaches and developed a questionnaire-suboptimal health status questionnaire-25 (SHSQ-25) for measuring SHS. The validity and reliability of this approach were evaluated in a small pilot study and then in a cross-sectional study of 3,405 participants in China. RESULTS: We found a correlation between SHS and systolic blood pressure, diastolic blood pressure, plasma glucose, total cholesterol and high-density lipoprotein (HDL) cholesterol among men, and a correlation between SHS and systolic blood pressure, diastolic blood pressure, total cholesterol, triglycerides and HDL cholesterol among women. CONCLUSIONS: The SHSQ-25 is a self-rated questionnaire of perceived health complaints, which can be used as a new instrument for PPPM. An ongoing longitudinal SHS cohort survey (China Sub-optimal Health Cohort Study, COACS) consisting of 50,000 participants will provide a powerful health trial to use SHSQ-25 for its application to PPPM through patient stratification and therapy monitoring using innovative technologies of predictive diagnostics and prognosis: an effort of paradigm shift from reactive to predictive medicine.

The genetic contribution of CIDEA polymorphisms, haplotypes and loci interaction to obesity in a Han Chinese population.

To investigate the association of tag-SNPs and haplotype structures of the CIDEA gene with obesity in a Han Chinese population. Five single nucleotide polymorphisms (SNPs) (rs1154588/V115F, rs4796955/SNP1, rs8092502/SNP2, rs12962340/SNP3 and rs7230480/SNP4) in the CIDEA gene were genotyped in a case-control study. Genotyping was performed using the sequenom matrix-assisted laser desorption/ionization time-of-flight mass spectrometry iPLEX platform. There were significant differences between the obese and control groups in genotype distributions of V115F (P < 0.001), SNP1 (P = 0.006) and SNP2 (P = 0.005). Carriers of V115F-TT, SNP1-GG and SNP2-CC genotypes had a 2.84-fold (95 % CI 1.73-4.66), 2.19-fold (95 % CI 1.09-4.38) and 4.37-fold (95 % CI 1.21-15.08) increased risk for obesity, respectively. Haplotype analysis showed that GTTC (SNP1/SNP2/V115F/SNP4) had 1.41-fold (95 % CI 1.02-1.95) increased risk for obesity; whereas, haplotype TTGC had 0.48-fold (95 % CI 0.24-0.96) decreased risk for obesity. Using the multifactor dimensionality reduction method, the best model including SNP1, SNP2, V115F and SNP4 polymorphisms was identified with a maximum testing accuracy to 59.32 % and a perfect cross-validation consistency of 10/10 (P = 0.011). Logistic analysis indicated that there was a significant interaction between SNP1 and V115F associated with obesity. Subjects having both genotypes of SNP1/GG and V115F/TT were more susceptible to obesity in the Han Chinese population (OR 2.66, 95 %: 1.22-5.80). Genotypes of V115F/TT, SNP1/GG and SNP2/CC and haplotype GTTC of CIDEA gene were identified as risk factors for obesity in the Han Chinese population. The interaction between SNP1 and V115F could play a joint role in the development of obesity.

The expected number of background disease events during mass immunization in China.

It is critical to distinguish events that are temporarily associated with, but not caused by, vaccination from those caused by vaccination during mass immunization. We performed a literature search in China National Knowledge Infrastructure and Pubmed databases. The number of coincident events was calculated based on its incidence rate and periods after receipt of a dose of hypothesized vaccine. We included background incidences of Guillain-Barré syndrome, anaphylaxis, seizure, sudden adult death syndrome, sudden cardiac death, spontaneous abortion, and preterm labour or delivery. In a cohort of 10 million individuals, 7.71 cases of Guillain-Barré syndrome would be expected to occur within six weeks of vaccination as coincident background cases. Even for rare events, a large number of events can be expected in a short period because of the large population targeted for immunization. These findings may encourage health authorities to screen the safety of vaccines against unpredictable pathogens.