生命早期抗生素使用与儿童哮喘:因果关系还是混杂效应?

儿童哮喘的发病率逐年上升.基于卫生假说,抗生素使用可能减少了微生物暴露,从而增加了过敏性疾病发生的风险.近十年来,就早期抗生素暴露与儿童哮喘的关系进行的大量的流行病学调查的结果并不一致.大多数回顾性研究发现正相关联系,但前瞻性研究未发现联系或联系强度较弱.逆向因果和指示混淆可部分解释两者的关系,但也难以否定因果关系的存在.     

Anti-goat antibodies as a rare cause of high gonadotropin levels during menopausal transition

Abstract

Objective: To understand the origin of extremely high gonadotropin levels in a perimenopausal woman.

Methods: A 52-year-old woman with a 2?months of amenorrhea followed spontaneous menstrual cycles recovery was referred to our outpatient clinic with elevated follicle-stimulating hormone (FSH, 483 mUI/ml), luteinizing hormone (LH, 475 mUI/ml) and prolactin (PRL, 173?ng/ml). She was known to take levosulpiride. The gonadotropin levels did not fit with the clinical features.

Results: A gonadotroph tumor was ruled out. Further analysis confirmed constantly high FSH, LH and PRL levels. The measurements were repeated using different analytical platforms with different results. After serial dilutions, nonlinearity was present suggesting an immunoassay interference. After post-polyethylene glycol recovery, hormone levels appeared in the normal range. Anti-goat antibodies were recognized in the serum of the patient.

Conclusions: This case report shows a case of falsely abnormal high gonadotropin and PRL levels in a woman during menopause transition. In the clinical practice the evaluation of gonadotropin profile is not recommended at this age, but the abnormal levels stimulated further evaluation. An interference in the assay due to anti-goat antibodies resulted in abnormally high level of FSH and LH. A strict collaboration between clinicians and the laboratory is needed, when laboratory findings do not correspond to clinical findings.     

Population Genetics Identifies Challenges in Analyzing Rare Variants

Geneticists have, for years, understood the nature of genome‐wide association studies using common genomic variants. Recently, however, focus has shifted to the analysis of rare variants. This presents potential problems for researchers, as rare variants do not always behave in the same way common variants do, sometimes rendering decades of solid intuition moot. In this paper, we present examples of the differences between common and rare variants. We show why one must be significantly more careful about the origin of rare variants, and how failing to do so can lead to highly inflated type I error. We then explain how to best avoid such concerns with careful understanding and study design. Additionally, we demonstrate that a seemingly low error rate in next‐generation sequencing can dramatically impact the false‐positive rate for rare variants. This is due to the fact that rare variants are, by definition, seen infrequently, making it hard to distinguish between errors and real variants. Compounding this problem is the fact that the proportion of errors is likely to get worse, not better, with increasing sample size. One cannot simply scale their way up in order to solve this problem. Understanding these potential pitfalls is a key step in successfully identifying true associations between rare variants and diseases.