When, as an analyst, you have established a correlation between two features of the data A and B, there are always five distinct families of hypothesis you should bear in mind to explain it:
1. A causes B. Perhaps obese or diabetic people choose to live near fast food restaurants?
2. B causes A. Perhaps fast food restaurants nearby encourage people to become obese?
3. A and B are caused by a separate factor C. Perhaps poor people are more likely to be obese or diabetic, and fast food outlets tend to open in poorer areas?
4. The data are a coincidence. Perhaps it's just chance that these two things occur together in the study data?
5. The data are wrong. Perhaps diabetics are more likely to be diagnosed in urban areas with more fast food restaurants, and rural diabetics are just not being picked up?
An observed correlation, by itself, will provide evidence in favour of hypotheses in any of these categories. Only additional features of the data will help you sort between them.
The website Spurious Correlations allows you to generate your own correlations from a number of data sources. As an analytical exercise, force yourself to come up with causal hypotheses to explain them.