Math-Physical Medicine

NO. 002

Using GH-Method: Math-Physical Medicine to investigate the macro-scaled relationship between Weather and Glucose

Corresponding Author: Gerald C. Hsu, eclaireMD Foundation, USA.

This paper discuss the direct relationship between ambient  temperature (weather) and glucose, both pasting plasma glucose (FPG) and postprandial plasma glucose (PPG).

The author suffered from severe type 2 diabetes for over 20 years.  After developing two prediction tools for FPG and PPG during 6/2015-3/2016, he was able to reduce his daily glucose from 279 mg/dL to 117 mg/dL, and A1C from 10% to 6.1%.  However, the prediction accuracy only reached to ~96%.

In addition to the primary factors causing his glucose fluctuations, such as medication, carbs and sugar intake, exercise, weight, he also examined other secondary factors, including anxiety, stress, measurement delay, traveling, illness, sleep disturbance, etc. In summary, more than 20 elements were considered and about 1.5 million data collected.

Between 5/2016 and 12/2017, he noticed his glucose fluctuation pattern correlated with the seasonal weather.  As a result, he started to investigate the ambient temperature effect.

He selected a period of 1,310 days (6/1/2015-12/31/2018) as his research window.  It included 3 weather periods: 562 days of warm temperature (43% days: >77 Fahrenheit); 371 days of mild temperature (28% days: 67-76 Fahrenheit); and 376 days of cold temperature (29% days: <66 Fahrenheit).

In his weather analysis, he used a curve-fitting method from engineering and developed and tried several different weather formulas to find the best fit.  In conclusion, for warm weather, the PPG increased by 0.9 mg/dL for every degree higher than 77 due to increased metabolism activities.  For cold weather, the FPG decreased by 0.3 mg/dL for every degree lower than 66 due to “hibernation” interpretation.

Figure 1: weather analysis during 6/1/2015 -12/31/2017
Figure 2: weather analysis during 6/1/2015 -12/31/2018

After adding into this temperature adjustment, his overall glucose prediction’s accuracy increases 2.2% from 97.6% (without weather) to 99.9% (with weather) and correlation coefficient decreases from 72% (without weather) down to 63% (with weather).

This analysis has proved that ambient temperature (weather) is one of the most important secondary factors for both FPG and PPG next to primary influential factors, such as carbs/sugar intake, post-meal exercise, and weight.