## 1 Introduction  
Cohort studies are widely used in epidemiology to measure how the exposure to certain factors influences the risk of a specific disease. The role of large cohort studies is increasing with the development of multi-omics approaches and with the search of methods for the translation of omics findings, especially those that are derived from genome-wide association studies (GWAS) in clinical settings [1]. Many research efforts have been made to link vast amounts of phenotypic data across diverse centers. This procedure concerns molecular information, as well as data regarding environmental factors, such as those recorded in and obtained from health-care databases and epidemiological registers [2]. Cohort studies can be prospective (forward-looking) or retrospective (backward-looking). Large-scale initiatives of cohort studies have been initiated worldwide, such as the NIG Roadmap Epigenomics Project [3] and the 500 Functional Genomics cohort [4] due to the increasing need of integration of data for genomic analysis. An example of the former is the Nurses' Health Study, a large prospective cohort study that revealed several significant associations between lifestyle choices and health by following up hundreds of thousands of women in North America [5]. Similarly, the National Health and Nutrition Examination Survey ([[http://www.cdc.gov/nchs/nhanes.htm](http://www.cdc.gov/nchs/nhanes.htm)]([http://www.cdc.gov/nchs/nhanes.htm](http://www.cdc.gov/nchs/nhanes.htm))) discovered the association between cholesterol levels and heart disease. Furthermore, another large prospective cohort study, the Framingham Heart Study ([[https://www.framinghamheartstudy.org](https://www.framinghamheartstudy.org)]([https://www.framinghamheartstudy.org](https://www.framinghamheartstudy.org))), demonstrated the cardiovascular health risks of tobacco smoking. However, the integration of data from multiple cohort studies faces a variety of challenges [6]. Database federation is a method for data integration that offers constant access to a number of various data sources in which middleware can operate, including interactive database management systems [6]. Individual-level data indicate information about participants, being either contributed by the participants themselves in surveys etc., or collected from registers. In fact, individual-level data assembling of large population-based studies across centers in international collaborations faces several difficulties [7]. On the one hand, merging cohort datasets extends the capability of these studies by allowing research questions of mutual interest to be addressed, by enabling the harmonization of standard measures, and by authorizing the investigation of a range of psychosocial, physical, and contextual factors over time. However, on the other hand, data are often collected in different locations and systems, and they are rarely aggregated into larger datasets, an aspect that limits their utility. Additionally, it is essential to accurately address privacy, legal, and ethical issues that are associated with data usage [8]. Meta-analysis is a common approach to pool results from multiple cohorts and it is consistently used, for example, in GWAS [9] epidemiological studies [10], case-control studies [11] and randomized controlled trials [12]. Several studies are pooled to increase the sample size to increase the statistical power to detect true association signals (decreasing type 1 error). Due to the restrictions of participants confidentiality and the refusal by authors to provide anonymized dataset, individual-level data often cannot be pooled among studies, so meta-analytical approaches are typically used to combine summary statistics across studies, and they have been shown to be as powerful as the integration of individual datasets [9]. In cohort studies, retaining participants with several waves of follow-up is challenging.