Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Filters applied. Clear all
. 2015 Sep;22(5):1042-53.
doi: 10.1093/jamia/ocv046. Epub 2015 Jun 2.

Birth Month Affects Lifetime Disease Risk: A Phenome-Wide Method

Affiliations
Free PMC article

Birth Month Affects Lifetime Disease Risk: A Phenome-Wide Method

Mary Regina Boland et al. J Am Med Inform Assoc. .
Free PMC article

Abstract

Objective: An individual's birth month has a significant impact on the diseases they develop during their lifetime. Previous studies reveal relationships between birth month and several diseases including atherothrombosis, asthma, attention deficit hyperactivity disorder, and myopia, leaving most diseases completely unexplored. This retrospective population study systematically explores the relationship between seasonal affects at birth and lifetime disease risk for 1688 conditions.

Methods: We developed a hypothesis-free method that minimizes publication and disease selection biases by systematically investigating disease-birth month patterns across all conditions. Our dataset includes 1 749 400 individuals with records at New York-Presbyterian/Columbia University Medical Center born between 1900 and 2000 inclusive. We modeled associations between birth month and 1688 diseases using logistic regression. Significance was tested using a chi-squared test with multiplicity correction.

Results: We found 55 diseases that were significantly dependent on birth month. Of these 19 were previously reported in the literature (P < .001), 20 were for conditions with close relationships to those reported, and 16 were previously unreported. We found distinct incidence patterns across disease categories.

Conclusions: Lifetime disease risk is affected by birth month. Seasonally dependent early developmental mechanisms may play a role in increasing lifetime risk of disease.

Keywords: cardiovascular diseases; electronic health records; embryonic and fetal development; maternal exposure; personalized medicine; pregnancy; prenatal nutritional physiological phenomena; seasons.

Figures

Figure 1:
Figure 1:
Overview of the SeaWAS algorithm. The algorithm takes all 1688 conditions as initial input, finds significant associations over all months, then it models each birth month’s association with the condition by smoothing the birth month proportions using a 2-month window. We then extracted all relevant birth month articles from PubMed (n = 92) and mapped the results to extractable codes from electronic health records. SeaWAS found 7 of the 16 diseases reported as associated with birth month in the literature corresponding to 19/55 associated codes.
Figure 2:
Figure 2:
SeaWAS Results Show Enrichments for Literature Associations. (A) shows the breakdown of SeaWAS results by number of publications demonstrating a relationship. (B) shows the number of SeaWAS associations known to be related to disease from the literature (solid black), and those that are closely related to known diseases (curvy lines). (C) Depicts all birth month–disease associations in a Manhattan plot organized by their respective ICD-9 disease categories (x axis). A significant SeaWAS association is a disease–birth month association remaining significant after FDR adjustment.
Figure 3:
Figure 3:
SeaWAS vs random reveals higher true positive rate, lower false positive rate, higher positive predictive value, and more confirmed literature associations. We used 1000 randomly generated samples. For each sample, 55 random codes were pulled (from the set of 1688), and then the number of confirmed literature associations was measured. SeaWAS consistently performed better than random across all measures.
Figure 4:
Figure 4:
Birth month distribution plots for 3 literature validated SeaWAS results and 3 discovered SeaWAS associations. We selected 3 well-known literature associations: asthma, ADHD, and reproductive performance to compare with SeaWAS birth month trends. We compared our results to findings published in articles for each of these diseases: 1) for asthma we used a Denmark study by Korsgaard et al.; 2) for reproductive performance we used an Austrian study by Huber et al., which we compared to full-term normal delivery (i.e., general birth code); and 3) for ADHD we used a Swedish study by Halldner et al. To facilitate comparison between asthma studies from different locales, we used data on the average monthly sunshine exposure for New York, USA and Skagen, Denmark obtained from World Weather and Climate Information., We also found 3 interesting new associations: atrial fibrillation, mitral valve disorder, and chronic myocardial ischemia.
Figure 5:
Figure 5:
Disease risk status breakdown by birth month illustrates disease category dependency. Some months, e.g., May, June, August, January, and December, provide no overall advantage or disadvantage to those born in that particular month (Figure 5, top). Other months, e.g., November, are more likely to be associated with increased disease risk while others, e.g., February, tend to be associated with decreased disease risk. The relationship between birth month and disease risk depends on disease category, and this is shown in the 4 lower subplots. Light gray lines represent risk curves for diseases belonging to a particular category. For example, individuals born in October are at increased risk for respiratory conditions and at the same time are at decreased risk for cardiovascular conditions.
Figure 6:
Figure 6:
SeaWAS cardiovascular condition-birth month proportions correlate with published lifespan-birth month results from Doblhammer et al. 2001. All 10 (9 novel) cardiovascular disease–birth month associations found by SeaWAS were compared to Doblhammer et al.’s lifespan-birth month dependencies for Denmark and Austria The lifespan-birth month associations are shown in Figure 6a. Six of the 10 were anti-correlated (i.e., months with low cardiovascular disease risk were also months with longer life expectancies from Doblhammer et al.’s study. The top 3 anti-correlated cardiovascular diseases are shown in Figure 6b, cardiac complications of care (Denmark: r = −0.815, P = .001; Austria: r = −0.863, P < .001); chronic myocardial ischemia (Denmark: r = −0.810, P = .001; Austria: r = −0.826, P < .001); and pre-infarction syndrome (Denmark: r = −0.712, P = .009; Austria: r = −0.918, P < .001). In Figure 6b, **denotes P ≤ 0.001 and *denotes P < .01 for both comparisons (Austria and Denmark).

Similar articles

See all similar articles

Cited by 31 articles

See all "Cited by" articles

References

    1. Hippocrates, Adams Ft. On Airs, Waters, and Places. http://classics.mit.edu/Hippocrates/airwatpl.mb.txt. 460BCE. Accessed August 7, 2014.
    1. McGrath JJ, Eyles DW, Pedersen CB, et al. Neonatal vitamin d status and risk of schizophrenia: A population-based case-control study. Arch General Psychiatr. 2010;67(9):889–894. - PubMed
    1. Halldner L, Tillander A, Lundholm C, et al. Relative immaturity and ADHD: findings from nationwide registers, parent- and self-reports. J Child Psychol Psychiatr. 2014;55(8):897–904. - PubMed
    1. Willer CJ, Dyment DA, Sadovnick AD, Rothwell PM, Murray TJ, Ebers GC. Timing of birth and risk of multiple sclerosis: population based study. BMJ. 2005;330(7483):120. - PMC - PubMed
    1. Huber S, Didham R, Fieder M. Month of birth and offspring count of women: data from the Southern hemisphere. Hum Reprod. 2008;23(5):1187–1192. - PMC - PubMed

Publication types

Feedback