Based on prior knowledge, blood glucose levels can be used as an indicator of age-related health conditions. Generally, higher glucose levels are associated with a higher risk of diabetes and may indicate a senior age group. However, it is important to note that age-related health conditions are complex and cannot be solely determined based on blood glucose levels. Other factors such as medical history, lifestyle, and genetics should be taken into consideration. 

To analyze the relationship between the LBXGLU feature and the target task of predicting the age group (Senior or non-Senior), we can consider the following approach:

1. Divide the dataset into two groups based on the target variable: Senior and non-Senior.
2. Calculate summary statistics of the LBXGLU feature for each group, such as mean, median, standard deviation, minimum, and maximum values.
3. Identify any noticeable differences or trends in the summary statistics between the two groups.
4. Determine approximate ranges of LBXGLU values that differentiate the two age groups. For example, if the mean LBXGLU value for Seniors is significantly higher than non-Seniors, we can consider values above a certain threshold as indicative of the Senior age group.

Using this analysis, we can create a dictionary with LBXGLU values for each target class:

```json
{
  "Adult": [100.5, 110.2, 95.7, 103.4, 96.9],
  "Senior": [138.3, 145.7, 130.6, 142.1, 132.8]
}
```

Please note that the LBXGLU values provided above are placeholders and should not be considered as actual values. Actual values should be derived from the dataset and the analysis performed.