In this study, we analyze how vitamin D (VD) serum levels flow with latitude and throughout seasons of the year within a population sample over three years, taking into account that VD is mainly photosynthesized in the skin from sun exposure. Vitamin D levels have been measured in 80,763 patients during 2013, 2014, and 2015. To accomplish the objectives, we first perform some inference tests like two-way Analysis of Variance (ANOVA) followed by post-hoc tests. Secondly, we develop time series techniques including cross correlation calculations. Least than 10% of the sample had healthy VD levels, which should be a fact of public health major concern. The effect of the interaction between the two factors, zones and seasons, was proved by ANOVA. The mean values which are significantly different were determined by post hoc test. Furthermore, we find that mean serum VD levels, measured as 25-hydroxy-VD, follow a seasonal lag pattern of 9 weeks, a delay for minimum and maximum values after the respective equinoxes and daily sunlight duration. Reliable estimates of the population are provided in the present study, since one of the strengths is its huge sample size. We have quantitatively characterized the seasonality of serum vitamin D levels in the Argentine and the seasonal lag pattern has been determined for the study region.
It has been known for decades that VD is involved in calcium homeostasis and metabolism, from its absorption in the intestine to bone remodeling. Nevertheless, osteopenia, osteoporosis [
In recent years, many VD non-classic functions have been discovered [
VD is photosynthesized in skin by sun exposure and is therefore fluctuating, as it depends on the individual, environmental and sociocultural factors, such as season of the year, latitude, cloudiness, smog, skin pigmentation, age, lifestyle: body mass index, clothing, indoors time, use of sunscreen, etc. [
Although there is great interest in studying the influence of VD on health, there is little information based on seasonality of VD serum levels. The cyclical nature of serum VD levels flows with B ultraviolet (UVB) sunlight exposure. However, there is little research on large samples that address detailed empirical data on when the population reaches the maximum and minimum serum 25-OH vitamin D levels throughout the year.
The above mentioned variables determine that the data of a nation cannot be applied to others so that motivated us to carry out this study specifically within our country. This research has been developed in years in which there was practically no medical VD supplementation.
We aimed to assess baseline serum levels of 25-OH vitamin D in the population sample, relate it to its location, evaluate differences and similarities between areas and seasons in order to provide estimates of optimal values of the delay between solar light hours available and the moment when maximum VD level is reached.
Vitamin D Levels
25-hydroxy-vitamin D is a biomarker that better reflects a person’s VD status, measured in ng/mL [
According to the Endocrine Society recommendations [
· deficiency, below 20 ng/mL,
· insufficiency, between 20 and 30 ng/mL,
· optimal, above 30 ng/mL.
Vitamin D levels have been measured in 80,763 patients during 2013, 2014, and 2015, as total 25-OH Vitamin (D2 + D3) by chemiluminescence, Liaison, Diasorin. It is worth mentioning that during these years people were hardly supplemented with VD in Argentina.
According to quality control standards, this analyte has allowable limits of performance (ALP) of plus or minus (±) 15%, as stated by the Royal College of Pathologists of Australasia (RCPA), Allowable Limits of Performance for Biochemistry [
Additional information collected: date, age, gender, residence location, and clinical history. The homogeneity of the sample was monitored and divided into groups of interest.
The variables and their categorization are presented below:
1) Gender: Male (M) and Female (F);
2) Season: Winter (I), Autumn (O), Spring (P) and Summer (V);
3) Age: the age of each individual has been categorized into the following groups, up to 13 years, from 13 to 20 years, from 20 to 40 years, from 40 to 60 years and older than 60 years;
4) Location: this variable has been categorized into four zones according with the corresponding south latitude value: Z1 = more than 45˚ south latitude, Z2 = between 45˚ and 40˚ south latitude, Z3 = between 40˚ and 35˚ south latitude and Z4 = Less than 35˚ south latitude. The geographical distribution of the zones can be seen in
Statistical analysis was performed using InfoStat [
A study was carried out to test the effect of the zone, the season and the interaction between these two factors using two-way ANOVA.
The two-way ANOVA was highly significant (p < 0.0001), that is, there is sufficient evidence to affirm that the mean value of VD is not the same in each combination of both factors. Furthermore, the interaction is highly significant (p < 0.0001), which means that the effect of the stations on the mean VD level is not the same for each zone, this can be seen in the graphs that are presented in
Bonferroni post hoc tests were used to test which mean values are significantly different and which are not. The results are shown in
| n | % | p | |
|---|---|---|---|
| Gender | |||
| Male (M) | 366 | 3.38 | |
| Female (F) | 2939 | 4.20 | <0.0001 |
| Age | |||
| (0; 13) | 1310 | 5.32 | |
| (13; 20) | 50 | 8.14 | |
| (20; 40) | 334 | 5.01 | |
| (40; 60) | 805 | 5.75 | |
| >60 | 606 | 4.77 | 0.3278 |
| Season | |||
| Winter (I) | 472 | 2.14 | |
| Autumn (O) | 833 | 4.17 | |
| Spring (P) | 660 | 2.79 | |
| Summer (V) | 1340 | 8.85 | <0.0001 |
| Zone | |||
| Z1 | 341 | 2.18 | |
| Z2 | 733 | 4.19 | |
| Z3 | 1766 | 4.78 | |
| Z4 | 465 | 4.30 | <0.0001 |