To analyze the relationship between the feature LBXGLU (Respondent's Blood Glucose after fasting) and the task of predicting the person's age group (Senior or non-senior), we can use our prior knowledge to determine the appropriate ranges of values for LBXGLU that are typically associated with each age group.

Based on general knowledge of blood glucose levels, we can assume that higher values of LBXGLU are more commonly observed in senior individuals compared to non-seniors. However, it is important to note that LBXGLU levels can vary depending on individual health conditions, medications, and other factors. Therefore, we cannot make precise cutoffs but can provide some typical ranges.

Let's create a dictionary with the LBXGLU values for each target class:

```json
{
	"Adult": [65.0, 83.5, 90.2, 95.7, 104.3],
	"Senior": [98.6, 105.1, 118.9, 127.4, 136.8]
}
```

In this dictionary, the key "Adult" represents the non-senior age group, and the key "Senior" represents the senior age group. For each target class, we have included five typical LBXGLU values as floats in the corresponding lists. These values are just examples and should not be considered as definitive cutoffs or ranges.

Please note that to provide more accurate and meaningful results specific to the dataset at hand, it is recommended to analyze a larger sample of data and consider using statistical techniques for feature selection and determining meaningful thresholds.