Based on the given feature description (LBXGLT) and the task of predicting a person's age group, here is an analysis of the relationship between the feature and target:

Based on prior knowledge and assuming that LBXGLT is a measure related to age group, we can hypothesize that LBXGLT values may be higher for seniors compared to non-seniors. This assumption is simply made based on the premise that as individuals age, they may experience changes in oral health, which might be reflected in LBXGLT measurements.

Please note that without more detailed information about the feature and its context, this analysis is based on general assumptions and prior knowledge.

Now, let's create the dictionary with the possible LBXGLT values for each target class:

```json
{
	"Adult": [50.1, 52.3, 54.8, 48.9, 51.2],
	"Senior": [62.5, 65.3, 59.2, 68.7, 63.1]
}
```

In this dictionary, the "Adult" key represents the target class "Adult" and the "Senior" key represents the target class "Senior". The corresponding values for each key are lists of example LBXGLT values for the respective target classes.

Please note that the provided values are just arbitrary examples and may not accurately represent the actual distribution of LBXGLT values for each target class.