Regional seropositivity for Borrelia burgdorferi and associated risk factors: findings from the Rhineland Study, Germany

Coors, Annabell; Hassenstein, Max J.; Krause, Gérard; Kerrinnes, Tobias; Harries, Manuela; Breteler, Monique M.B.; Castell, Stefanie

doi:10.1186/s13071-022-05354-z

Cited by 9 publications

(12 citation statements)

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“…Assay and instrument compatibility with both 96-and 384well plates allows the Borrelia multiplex assay to be used as an efficient screening tool for rapidly analyzing large sample sets, such as national studies 20 . Manual performance of the assay remains possible for smaller sample sets using smaller 96-well plates.…”

Section: Discussionmentioning

confidence: 99%

Simultaneous Detection of Different Antibody Classes in a Multiplexed Serological Test

Häring

Michel

Becker

et al. 2023

JoVE

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To monitor the progression of infectious diseases, it is useful to assess immunoreactivity against various antigenic determinants, and measure different antibody isotypes because they appear at different stages of the host immune response. With Lyme borreliosis, the pathogenic agent can be one of the multiple members of the Borrelia species. Therefore, correct sample classification requires evaluating the immunoreactivity against different antigens of different Borrelia species.Additionally, anti-pathogen IgG and IgM responses can have different elicitation time courses during disease progression. Here we demonstrate the development of a tworeporter multiplex immunoassay that has utility in identifying Borrelia-specific immune response in human serum samples by simultaneously evaluating both IgG and IgM immunoreactivity against different bacterial antigens in the same reaction well. This dual-reporter approach retains the analytical performance of single-reporter methods while conserving time and resources and reducing sample size requirements. This assay allows essentially double the serological information to be generated from a blood sample in half the time.

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Section: Discussionmentioning

confidence: 99%

Simultaneous Detection of Different Antibody Classes in a Multiplexed Serological Test

Häring

Michel

Becker

et al. 2023

JoVE

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“…complex contains numerous genospecies, but only a few can infect humans and cause Lyme disease. The genospecies that can infect humans have distinct geographic distributions: Borrelia burgdorferi sensu stricto is primarily found in North America, whereas Borrelia afzelii and Borrelia garinii are both prevalent in Asia and Europe [ 18 , 19 ]. Furthermore, the clinical manifestations of Lyme disease vary depending on the genospecies involved in the infection, and thus, the symptoms also vary by geographical region.…”

Section: Introductionmentioning

confidence: 99%

“…The infectious agent is transmitted to humans by several species of ticks from the Ixodes genus, whose distribution varies geographically. Ixodes scapularis and Ixodes pacificus are the most prevalent in North America, Ixodes ricinus is the most prevalent in Europe, and Ixodes persulcatus is the Lyme disease vector in Asia [16,18,[22][23][24]. Tick vectors progress through sequential life stages: egg, larva, nymph, and adult.…”

Section: Introductionmentioning

confidence: 99%

Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis

Laison,

Hamza Ibrahim,

Boligarla

et al. 2023

J Med Internet Res

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Background Lyme disease is among the most reported tick-borne diseases worldwide, making it a major ongoing public health concern. An effective Lyme disease case reporting system depends on timely diagnosis and reporting by health care professionals, and accurate laboratory testing and interpretation for clinical diagnosis validation. A lack of these can lead to delayed diagnosis and treatment, which can exacerbate the severity of Lyme disease symptoms. Therefore, there is a need to improve the monitoring of Lyme disease by using other data sources, such as web-based data. Objective We analyzed global Twitter data to understand its potential and limitations as a tool for Lyme disease surveillance. We propose a transformer-based classification system to identify potential Lyme disease cases using self-reported tweets. Methods Our initial sample included 20,000 tweets collected worldwide from a database of over 1.3 million Lyme disease tweets. After preprocessing and geolocating tweets, tweets in a subset of the initial sample were manually labeled as potential Lyme disease cases or non-Lyme disease cases using carefully selected keywords. Emojis were converted to sentiment words, which were then replaced in the tweets. This labeled tweet set was used for the training, validation, and performance testing of DistilBERT (distilled version of BERT [Bidirectional Encoder Representations from Transformers]), ALBERT (A Lite BERT), and BERTweet (BERT for English Tweets) classifiers. Results The empirical results showed that BERTweet was the best classifier among all evaluated models (average F1-score of 89.3%, classification accuracy of 90.0%, and precision of 97.1%). However, for recall, term frequency-inverse document frequency and k-nearest neighbors performed better (93.2% and 82.6%, respectively). On using emojis to enrich the tweet embeddings, BERTweet had an increased recall (8% increase), DistilBERT had an increased F1-score of 93.8% (4% increase) and classification accuracy of 94.1% (4% increase), and ALBERT had an increased F1-score of 93.1% (5% increase) and classification accuracy of 93.9% (5% increase). The general awareness of Lyme disease was high in the United States, the United Kingdom, Australia, and Canada, with self-reported potential cases of Lyme disease from these countries accounting for around 50% (9939/20,000) of the collected English-language tweets, whereas Lyme disease–related tweets were rare in countries from Africa and Asia. The most reported Lyme disease–related symptoms in the data were rash, fatigue, fever, and arthritis, while symptoms, such as lymphadenopathy, palpitations, swollen lymph nodes, neck stiffness, and arrythmia, were uncommon, in accordance with Lyme disease symptom frequency. Conclusions The study highlights the robustness of BERTweet and DistilBERT as classifiers for potential cases of Lyme disease from self-reported data. The results demonstrated that emojis are effective for enrichment, thereby improving the accuracy of tweet embeddings and the performance of classifiers. Specifically, emojis reflecting sadness, empathy, and encouragement can reduce false negatives.

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“…Previous research considering the notification and serological data suggests several risk factors for human seropositivity, with advancing age and male sex as influential factors [21][22][23][24]. However, surveillance and health insurance data display a more complex representation of the age distribution: a bimodal distribution of cases over age with an elevated incidence and diagnoses in children aged 5 to 9 years and adults aged 50 to 69 [6,16,25].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, existing research has not yet clarified the role of socioeconomic status (SES) conclusively, as the studies use a different methodology. For example, the use of income on the neighborhood/municipality level versus the use of education level on the individual level found differing results [24,[26][27][28][29][30]. The interrelationship between SES, living environment, profession, and lifestyle, including recreational activity, may affect tick exposure, and, therefore, past infections are reflected in part by the serostatus.…”

Section: Introductionmentioning

confidence: 99%

Seroepidemiology of Borrelia burgdorferi s.l. among German National Cohort (NAKO) Participants, Hanover

et al. 2022

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Lyme borreliosis is the leading tick-related illness in Europe, caused by Borrelia Burgdorferi s.l. Lower Saxony, Germany, including its capital, Hanover, has a higher proportion of infected ticks than central European countries, justifying a research focus on the potential human consequences. The current knowledge gap on human incident infections, particularly in Western Germany, demands serological insights, especially regarding a potentially changing climate-related tick abundance and activity. We determined the immunoglobulin G (IgG) and immunoglobulin M (IgM) serostatuses for 8009 German National Cohort (NAKO) participants from Hanover, examined in 2014–2018. We used an enzyme-linked immunosorbent assay (ELISA) as the screening and a line immunoblot as confirmation for the Borrelia Burgdorferi s.l. antibodies. We weighted the seropositivity proportions to estimate general population seropositivity and estimated the force of infection (FOI). Using logistic regression, we investigated risk factors for seropositivity. Seropositivity was 3.0% (IgG) and 2.1% (IgM). The FOI varied with age, sharply increasing in participants aged ≥40 years. We confirmed advancing age and male sex as risk factors. We reported reduced odds for seropositivity with increasing body mass index and depressive symptomatology, respectively, pointing to an impact of lifestyle-related behaviors. The local proportion of seropositive individuals is comparable to previous estimates for northern Germany, indicating a steady seroprevalence.

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Regional seropositivity for Borrelia burgdorferi and associated risk factors: findings from the Rhineland Study, Germany

Cited by 9 publications

References 50 publications

Simultaneous Detection of Different Antibody Classes in a Multiplexed Serological Test

Simultaneous Detection of Different Antibody Classes in a Multiplexed Serological Test

Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis

Seroepidemiology of Borrelia burgdorferi s.l. among German National Cohort (NAKO) Participants, Hanover

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