In the previous post, we looked at the hypothesis that increasing environmental temperature synchronises desynchronised circadian rhythms in Bipolar Depression. As a hypothesis, this would need to be investigated to accumulate the evidence to either confirm or refute this.
We know that maximum environmental temperature decreases on moving from the equator to the poles. Therefore a secondary hypothesis might be that the prevalence of Bipolar Depression might increase on moving from the equator to the poles. There are a number of other factors however that would likely invalidate such a hypothesis or else would make it difficult to investigate which I will cover below.
Factors Interacting with Environmental Temperature and Latitude
There are two important factors that influence the environmental temperature at a given latitude – continentality and altitude.
The term continentality refers to the effects of a continent on climate – specifically exemplified by temperature variation. The sea stabilises environmental temperature (evaporation being an effective method of dispersing heat). Moving away from coastal areas, further inland will be associated with an increasing variation in environmental temperature. Continentality was initially characterised by Alexander Von Humboldt. There are other factors that influence this relationship including the presence of mountain ranges between the land and sea.
Another important factor is altitude again characterised by Alexander Von Humboldt. The higher the altitude, the lower the temperature. This is a little bit of a simplification as there are a number of other factors that influence this relationship. Here is a neat post outlining a simple model of the relationship between altitude and temperature along with a description of modifying factors.
Modification of the Environment
People adapt to their environment – the ability to adapt to the environment is an important evolutionary driver. It should therefore come as no surprise that people are very good at stabilising (indoor) environmental temperatures. If we take a step back and look at whether latitude influences mood via environmental temperature, air conditioning and other environmental modifications are likely to confound this. The effects of changes in temperature will still be experienced even with these adaptations in most cases as the modifications are not perfectly efficient. Nevertheless measuring the external temperature will give a potentially misleading picture of the environmental temperature for indoor workers. Therefore in terms of selecting populations to explore these phenomenon, outdoor workers may be more likely to experience the effects of changes in environmental temperature.
From Biology to Hospital Admission
A number of registry studies often include hospital admissions with diagnosis. I mention registry studies as they are a good way to obtain big data sets to support an examination of the relationship between variables.
However, if we are looking at a simple biological relationship – between mood and environmental temperature, a simple measure such as hospital admission is many steps removed from biology.
Firstly let us consider the biology. Returning to the hypothesis that in Bipolar Depression a switch to Bipolar Mania is more probable with a higher environmental temperature. If this phenomenon exists, then let us suppose that there are X instances of this at any given time.
In order for us to be able to identify this, we then need the biological phenomenon to be translated into a clinical interpretation. This would require that the person comes to the attention of services, that the episode is promptly identified and correctly diagnosed. Let us suppose that Y cases come to the attention of services and receive a diagnosis.
Y = aX
where a is a clinical identification coefficient
The clinical identification coefficient would vary between different regions according to factors such as the configuration of local services, health seeking behaviour and factors affecting health seeking behaviour (e.g. the prominence of the symptoms).
Once identified clinically, there may be a large number of factors determining whether hospital admission is required. Let us say that Z is the number of admissions, then
Z = bY
where b is the admission coefficient
The admission coefficient would be influenced by the number of hospital beds, configuration of local community services, clinical evaluation of severity and a number of other factors.
On moving from biology to clinical identification to hospital admission, there are a number of transformations of the population size which are determined by various factors. By simply looking at hospital admissions, the transformations may prevent us from clearly seeing the effects on biology.
The primary hypothesis results from a consideration of circadian rhythms but we should also consider the cycles of environmental temperature. The pattern of morning stability of temperature with an increase later in the day and decrease at night reflects the circadian rhythm. During the daytime, the sun heats the ground increasing the surface temperature and the heat is radiated back from the ground with a cooling through the night. There is a diurnal temperature variation. By simply taking single snapshots of environmental temperature and another variable (e.g. body temperature), we will miss the nuances of this relationship and potentially the phenomenon under investigation.
If we are looking at the effects of extremes of temperature, we should factor in whether these are likely to be realistic. Take polar research stations for instance. The temperatures at the poles are so cold that human survival is not possible unless the person is shielded from the environmental temperature by extreme environmental modifications. Under these circumstances, the results may be unexpected. A very low temperature of -89.2 degree Celsius was recorded in Antarctica. This is significantly below the average body temperature (approximately 125.7 degrees Celsius below). On the other hand, the highest recorded temperatures on Earth are between 50 and 70 degrees Celsius although higher values are possible theoretically. These values differ from the average body temperature by between approximately 13.5 and 33.5 degrees Celsius. This is a much smaller difference than that recorded in Antarctica.
Summarising – for cold conditions, the coldest places in the world are so cold that humans can survive there only by creating completely artificial environments. These environments prevent us from seeing the effects of environmental temperature on the human body. On the other hand, the hottest places on Earth are not so different from the average body temperature although they are still sufficient to make them almost uninhabitable. Thus if we were to select regions of interest for such a study we would have to exclude a number of the very coldest areas on the planet and some of the hottest places.
Towards a Final Model
So if we were to investigate the primary hypothesis that higher environmental temperatures are likely to synchronise desynchronised circadian rhythms in Bipolar Depression and how this links in with latitude we would have to restrict the variables. We would need to select populations of outdoor workers. If there are outdoor workers then it would mean that the conditions are suitable for outdoor working although we should be careful to select for suitable environmental temperature limits. We would need to select locations according to latitude, continentality and altitude.
Then importantly we would need to measure variables which are as close to the biology as possible whilst being clinically relevant. Self-rating measures of mood would be needed, clinically validated diagnoses and identification of Depression and Mania. There should be good datasets on daily body temperature measured through the course of the day for all days of interest. We should also have climate data for all the days of interest. Such an investigation would not be well suited to retrospective collection of demographic data but would need to be prospective, multicentre albeit with small numbers* and recording many variables several times on a daily basis.
* Small numbers will still generate a lot of data to investigate the primary hypothesis although this does not preclude larger studies. A power calculation can use data from the studies referenced in the previous posts.
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