Reference Data for new Phenotypes

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1. Background
Reference intervals and normative data are important in medicine sciences and clinical care to characterize measured data, e.g., to find outliers. A typical way to represent such reference intervals in medical sciences is to use percentile curves specifically for each gender and separated by age bands based on a large population. In this way, a new measured value can be compared to most frequent values of this population.
2. Problem
Reference intervals and normative data are traditionally available for anthropometry and within laboratories in order to decide whether measured values are out of range and to trigger actions if necessary. However, such data are not available for new phenotypes as they have been measured in large epidemiological studies, such as LIFE Adult and LIFE Heart . That makes it difficult to characterize such data on individual level. For example, given a female person of 42 years for who a hand grip power of 32 kg is measured. The question whether 32 kg is low, high or normal in contrast to other people in Mid Europe can be only answered by taking reference intervals or normative data into account.
3. Solution
We used data of 10,000 adults out of the epidemiological study LIFE Adult to generate reference intervals / normative data for different new phenotypes including hand grip power, anthropometry, blood pressure, different measurements of eyes and in phonatry (voices). We used the widely accepted GAMLSS approach to analyze raw data and to generate percentile curves according to gender and age bands. The percentile curves are used to visualize data, i. e., individual data of a person which are then related to the LIFE Adult population data and percentile curves.
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