Based on my prior knowledge, the feature LBXGLU, which represents the respondent's blood glucose level after fasting, can be used to predict age groups. Typically, higher blood glucose levels are associated with senior age groups, while lower levels are associated with non-senior age groups.

Using this information, I will conduct an analysis of the relationship between the LBXGLU feature and the target variable, age group.

Here is the dictionary with the feature values for each target class:

```json
{
	"Adult": [78.6, 83.2, 91.5, 95.1, 98.3],
	"Senior": [103.7, 110.2, 116.8, 120.6, 125.4]
}
```

These values are just examples and may not accurately represent the actual ranges of values for each age group. To create a more accurate dictionary, it would be necessary to analyze a dataset containing actual blood glucose values for different age groups and determine appropriate ranges based on statistical analysis.