Graphical Interpretation In GAMSAT

A graph is a visual representation of the relationship between two or more variables (X and Y variables). They enable more straightforward comprehension of data and make the process of interpretation more efficient, as it may be inefficient and cumbersome to report data in a table (or worse, still in written form).

Typically, there will be an independent variable (the X-axis, usually time or some intervention) and the dependent variables (the Y-axis). When assessing a graph, take the time to understand the variables and to hypothesise what the relationship may be prior to looking at the body of the graph. In many cases you will be able to hypothesise the relationship based on knowledge from your studies. Students who fail to do this are likely to become confused when looking at the graph. After predicting what the relationship might be based on your knowledge, you can refer to the body of the graph for the depicted relationship. At this point, you are not interested in forming conclusions, only reporting what you see. The best way to do this is to verbalise your findings: “As <X-variable> increases, <Y-variable> increases/decreases in a direct/exponential manner.” In some cases, there may be no change in the dependent variable as the independent variable changes (in which case there is no relationship).

Below is a common graph seen in GAMSAT — and commonly confusing for students — which pertains to glucose excretion depending on serum concentration. 

Glucose excretion from the kidneys is dependent on its serum concentration. 

When we examine the figure, we can deduce that the independent variable is the amount of sugar in the blood. The dependent variables are the amount of sugar filtered to form filtrate (not yet urine), the amount of glucose reabsorbed into the blood from the filtrate, and the amount of glucose that is excreted in urine (formally filtrate, and not reabsorbed).

Firstly, consider the relationship between serum glucose and glucose in the filtrate. Without looking at the graph, you will know that this will be a positive and direct relationship. At this point, we are not interested in the other two dependent variables. As serum glucose goes up, filtered glucose will go up. Assessing the graph, we can see this is indeed the case.

Secondly, the relationship between serum glucose and glucose reabsorption from filtrate back into blood can be considered. With knowledge of renal physiology, we would expect that to a certain serum (and therefore filtrate) concentration that all glucose in the filtrate would be reabsorbed, but that at some point the resorptive function would become saturated and over a window of concentration that decreasing amount of additional glucose could be absorbed. The relationship would therefore initially be positive and direct before flattening out after which no relationship would exist. This is indeed the case when looking at the purple line in the graph shown.

Finally, consider the relationship between serum glucose and excretion of glucose. As serum glucose increases, we would expect glucose excretion to only increase once reabsorption is saturated. The relationship should therefore commence flat prior to rising and then becoming positive and direct.