Seasonal Variation in Diabetes Onset and Course of Disease
Jacob Schor, ND
April 15, 2006
Subject: March should be officially dedicated as Diabetes Month. People are more likely to get diagnosed with diabetes in March than any other time of the year and if you are diabetic you will have more problems with the disease in March than any other time of the year.
Granted I am a month late in writing this article. March is a bad time of year for diabetes. But April isn't that much better so the information is still relevant.
A study published 8 years ago compared the seasonal variations in type 1 diabetes onset between Sardinia and Finland . Apparently the researchers were hoping for some evidence that would point to a viral etiology. Instead they found somewhat similar patterns of onset for the disease. In Sardinia younger people had a lower incidence on disease onset from May through August, while in Finland disease onset was lowest for younger people in June. [i]
A study published last year of U.S. veterans suggested that people with type 2 diabetes have the greatest difficulty maintaining healthy concentrations of blood glucose in March and April. The investigators tracked to seasonal variations in monthly hemoglobin A 1c (A1c) values over 2 years from October 1998 to September 2000) among US diabetic veterans. The study included 285,705 veterans with 856,181 A1c tests. A1c values were higher in winter and lower in summer. The seasonal variation was consistent across different subpopulations. Regions with colder winter temperatures had larger winter-summer contrasts than did those with warmer winter temperatures. [ii]
The most recent study was published in the January issue of Diabetes Care and suggests that a person's likelihood of developing type 2 diabetes is about 50 percent higher in March than in August.
Led by Péter Doró, researchers at the University of Szeged in Hungary analyzed 26,695 cases of the disease that arose in one Hungarian county between 1999 and 2004. The scientists recorded diabetes onset as the date on which each patient first received drugs to reduce blood glucose.
Type 2 diabetes onset peaked in March, at about 10 cases per 10,000 county residents, and fell in subsequent months to a low of 6.8 cases per 10,000 residents in August. Onset rose after August. [iii]
According to an article published in Science News (Science News February 4, 2006 Vol 169, pg 77), Doró and his colleagues did not offer an explanation for these variations, and in a subsequent issue, David Motseller wrote offering the obvious:
This criticism it turns out was inappropriate. In a private communication the lead researcher, Peter Doró, clarified the situation to me:
The full text of the study by Doró et al can be found on their website at: [add link]
My prior newsletter written in 2004 on the links between vitamin D and diabetes can be read at:
At this point in time, the information on vitamin D and diabetes is clear enough that it is reasonable to suggest that anyone at risk for developing diabetes or who has early stage disease should consider daily vitamin D supplementation, especially during the winter months when their own production decreases due to lack of regular sun exposure.
These studies also provide one very good excuse to get outside on these nice spring days and enjoy the sunshine.
[i] Diabetes Care, Vol 21, Issue 7 1101-1109, Copyright © 1998 by American Diabetes Association
Comparison of the seasonal pattern in the clinical onset of IDDM in Finland and Sardinia
Karvonen, V Jantti, S Muntoni, M Stabilini, L Stabilini, S Muntoni and
To examine the seasonal pattern for the clinical onset of IDDM in Finland
and Sardinia , two areas where the incidence of IDDM is the highest in
the world, and to determine the effect of climate and temperature on the
clinical onset of IDDM. RESEARCH DESIGN AND METHODS: Analysis of seasonality
for the diagnosis of IDDM was based on 1,405 cases in Finland and 425
cases in Sardinia diagnosed at < or = 14 years of age from 1989 to
1992. The average annual incidence of IDDM was 36.4/100,000 in Finland
and 34.4/100,000 in Sardinia . Seasonal patterns were estimated presenting
the data as short Fourier series up to three harmonics together with a
possible linear trend. Likelihood ratio tests and Akaike's information
criterion were used to determine the number of harmonics necessary to
model the seasonal pattern. Seasonal patterns in both countries were compared
between sexes and between the three 5-year age-groups, each controlling
for the other's effect. RESULTS: In both countries, a significant seasonal
pattern during a calendar year was found for the sexes combined and for
two age-groups (0-9 and 10-14 years). In Sardinia , two distinct cycles
were found in the younger age-group, with a decreased incidence during
May through August and an increased incidence during the autumn months.
Two cycles were apparent in the older age-group, with the nadir occurring
during June through September. In Finland , one cycle was found in the
younger age-group, with a decreased incidence in June. In the older age-group,
there were two distinct cycles, with a decreased incidence in June and
in the September through December period. CONCLUSIONS: Differences between
Finland and Sardinia in the seasonal pattern for the incidence of newly
diagnosed IDDM cannot be explained by differences in climate, temperature,
a longer warm period in Sardinia, or other climatic phenomena. The results
do not provide evidence in favor of a specific viral etiology of IDDM.
It may be suggested that there are triggering events at certain times,
but they are likely to be unspecific. Nevertheless, why the incidence
of IDDM in these two populations is equally high despite differences in
climate, environment, and genetic background remains an unsolved question.
[ii] American Journal of Epidemiology
American Journal of Epidemiology 2005 161(6):565-574; doi:10.1093/aje/kwi071
Seasonal Patterns in Monthly Hemoglobin A 1c Values
Chin-Lin Tseng1,2, Michael Brimacombe1,2, Minge Xie3, Mangala Rajan1, Hongwei Wang3, John Kolassa3, Stephen Crystal4, Ting-Cheng Chen5, Leonard Pogach1,6 and Monika Safford1,6,7
Center for Health Care Knowledge Management, Department
of Veterans Affairs New Jersey Health Care System, East Orange, NJ
Correspondence to Dr. Chin-Lin Tseng, Department of Veterans Affairs New Jersey Health Care System, East Orange VA Medical Center, 385 Tremont Avenue, #129, East Orange, NJ 07018 (e-mail: Tseng@njneuromed.org ).
The purpose of this study was to investigate seasonal variations in population monthly hemoglobin A 1c (A1c) values over 2 years (from October 1998 to September 2000) among US diabetic veterans. The study cohort included 285,705 veterans with 856,181 A1c tests. The authors calculated the monthly average A1c values for the overall population and for subpopulations defined by age, sex, race, insulin use, and climate regions. A1c values were higher in winter and lower in summer with a difference of 0.22. The proportion of A1c values greater than 9.0% followed a similar seasonal pattern that varied from 17.3% to 25.3%. Seasonal autoregressive models including trigonometric function terms were fit to the monthly average A1c values. There were significant seasonal effects; the seasonal variation was consistent across different subpopulations. Regions with colder winter temperatures had larger winter-summer contrasts than did those with warmer winter temperatures. The seasonal patterns followed trends similar to those of many physiologic markers, cardiovascular and other diabetes outcomes, and mortality. These findings have implications for health-care service research in quality-of-care assessment, epidemiologic studies investigating population trends and risk factors, and clinical trials or program evaluations of treatments or interventions.
diabetes mellitus; hemoglobin A, glycosylated; seasons; veterans
Seasonality in the Incidence of Type 2 Diabetes
A population-based study
Péter Doró, MPHARM, Ria Benk , MPHARM, Mária Matuz, MPHARM and Gyöngyvér Soós, PHD
From the Department of Clinical Pharmacy, University of Szeged , Szeged , Hungary
Address correspondence to Péter Doró, Szikra u.8., Szeged , H-6725, Hungary . E-mail: firstname.lastname@example.org
The seasonal pattern in the onset of type 1 diabetes has been described ( 1 ), but seasonality in the onset of type 2 diabetes has not been previously reported. Some studies revealed seasonal changes in glycemic control in selected cohorts of patients with type 2 diabetes ( 2 – 4 ). While the incidence of type 1 diabetes can be easily studied based on complete registries, the onset of type 2 diabetes is more difficult to identify and study because in the earliest stage of the disorder, mostly nonmedical approaches are applied that are not always recorded in the medical profile. In the progression of type 2 diabetes, the initiation of treatment with oral antihyperglycemic drugs is the stage at which all patients can be definitively recognized as having the disorder.
We identified all incident cases of type 2 diabetes ( n = 26,695) for the entire population of Csongrád County , Hungary ( n = 430,000), between January 1999 and December 2004. Information was retrieved from the database of the Hungarian National Health Fund Administration, which provides a complete history of prescription drug use of the population at the patient level. Incident cases were defined as patients who had received no antidiabetic therapy during the 12-month period prior the initiation of an oral antihyperglycemic drug.
For quantifying the strength of seasonality, an autoregressive regression model was fitted to the monthly data and the coefficient of determination ( R 2 Autoreg ) calculated ( 5 ). The results of the regression revealed a strong seasonality ( R 2 Autoreg = 0.632). Seasonality followed a sinusoidal pattern; the peak month was March, with a monthly incidence of 430.3 ± 34.0 (means ± SD) cases, and the trough month was August, with a monthly incidence of 293.2 ± 23.8 cases. Similar patterns were found in both sexes. The months of peak and trough coincide with the peak and trough periods in the seasonality of HbA 1c values previously reported ( 2 ).
[iv] Science News April 8, 2006 Vol 169, No 14 pg 223