7496 Differences in seasonality's influence on individual lipid parameters: A time series analysis

Abstract Disclosure: N. Edo: None. T. Sawa: None. A. Terakawa: None. T. Uchino: None. H. Yamazaki: None. K. Uno: None. K. Uno: None. K. Morita: None. T. Ishikawa: None. T. Teramoto: None. K. Tsukamoto: None. Introduction: Many studies have reported seasonal changes in serum lipid levels. However, how much the seasonality affects the individual lipid parameters is still undetermined. Therefore, this study aimed to evaluate the differences in seasonality's influence on lipid parameters. Participants and Methods: We retrospectively collected data on TC, HDL-C, LDL-C, and TG levels measured between March 2016 and February 2021. All participants were over 18 years old and were included in this study regardless of underlying disease or treatment status. Next, we made two datasets: one with simultaneous TC, HDL-C, and TG measurements (Group A, n = 137,964) and the other with simultaneous HDL-C, LDL-C, and TG measurements (Group B, n = 119,614). Non-HDL-C and LDL-C (Friedewald: F) levels were calculated in Group A. Using the monthly median levels, we made monthly time series data for each sex. Upon adjusting for the monthly mean age, the data were decomposed into seasonal component (St), trend component, and remainder component (Rt), using seasonal and Trend decomposition using Loess method, which was performed by the "forecast::stl()" function of R software. Finally, the degree of seasonality's influence was evaluated by calculating the following parameters using the dispersions of each component: St/(St + Rt). Results: In Group A, the parameters calculated were as follows: TC; 0.78, HDL-C; 0.79, non HDL-C; 0.49, LDL-C (F); 0.63, and TG; 0.35 for male individuals, and TC; 0.76, HDL-C; 0.81, non HDL-C; 0.37, LDL-C (F); 0.51, and TG; 0.44 for female individuals. In Group B, these were as follows: HDL-C; 0.81, LDL-C; 0.28, and TG; 0.32 for male individuals, and HDL-C; 0.84, LDL-C; 0.28, and TG; 0.33 for female individuals. Discussion: Our results suggest that the effects of seasonality were more evident in TC and HDL-C levels and that they were more subtle in non-HDL-C, measured LDL-C, and TG levels. Therefore, considering in which season they are measured would be preferable when evaluating the HDL-C level. On the contrary, non-HDL-C and directly measured LDL-C levels might be able to be assessed, regardless of the season. Presentation: 6/3/2024

Authors

Institutions

Publication Details

Journal
Journal of the Endocrine Society
Published
2024-10-01
DOI
https://doi.org/10.1210/jendso/bvae163.573
Citations
1
Primary Topic
Climate Change and Health Impacts
Type
article
Field-Weighted Citation Impact
0.31
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

7496 Differences in seasonality's influence on individual lipid parameters: A time series analysis

T. Ishikawa, Naoki Edo, Tairin Uchino, Tomohiro Sawa et al.
1 citations
Journal of the Endocrine Society
Climate Change and Health Impacts
0.31
article

7496 Differences in seasonality's influence on individual lipid parameters: A time series analysis

T. Ishikawa, Naoki Edo, Tairin Uchino, Tomohiro Sawa, A Terakawa, Hiroki Yamazaki, Kazuhisa Tsukamoto, Koji Morita, T Teramoto, K. Uno
article en
1 citations

Abstract

Abstract Disclosure: N. Edo: None. T. Sawa: None. A. Terakawa: None. T. Uchino: None. H. Yamazaki: None. K. Uno: None. K. Uno: None. K. Morita: None. T. Ishikawa: None. T. Teramoto: None. K. Tsukamoto: None. Introduction: Many studies have reported seasonal changes in serum lipid levels. However, how much the seasonality affects the individual lipid parameters is still undetermined. Therefore, this study aimed to evaluate the differences in seasonality's influence on lipid parameters. Participants and Methods: We retrospectively collected data on TC, HDL-C, LDL-C, and TG levels measured between March 2016 and February 2021. All participants were over 18 years old and were included in this study regardless of underlying disease or treatment status. Next, we made two datasets: one with simultaneous TC, HDL-C, and TG measurements (Group A, n = 137,964) and the other with simultaneous HDL-C, LDL-C, and TG measurements (Group B, n = 119,614). Non-HDL-C and LDL-C (Friedewald: F) levels were calculated in Group A. Using the monthly median levels, we made monthly time series data for each sex. Upon adjusting for the monthly mean age, the data were decomposed into seasonal component (St), trend component, and remainder component (Rt), using seasonal and Trend decomposition using Loess method, which was performed by the "forecast::stl()" function of R software. Finally, the degree of seasonality's influence was evaluated by calculating the following parameters using the dispersions of each component: St/(St + Rt). Results: In Group A, the parameters calculated were as follows: TC; 0.78, HDL-C; 0.79, non HDL-C; 0.49, LDL-C (F); 0.63, and TG; 0.35 for male individuals, and TC; 0.76, HDL-C; 0.81, non HDL-C; 0.37, LDL-C (F); 0.51, and TG; 0.44 for female individuals. In Group B, these were as follows: HDL-C; 0.81, LDL-C; 0.28, and TG; 0.32 for male individuals, and HDL-C; 0.84, LDL-C; 0.28, and TG; 0.33 for female individuals. Discussion: Our results suggest that the effects of seasonality were more evident in TC and HDL-C levels and that they were more subtle in non-HDL-C, measured LDL-C, and TG levels. Therefore, considering in which season they are measured would be preferable when evaluating the HDL-C level. On the contrary, non-HDL-C and directly measured LDL-C levels might be able to be assessed, regardless of the season. Presentation: 6/3/2024

Journal of the Endocrine SocietyVol. 8(Supplement_1)
Teikyo University (JP)
Openalex Percentile: Top 37%
Climate Change and Health Impacts
0.31
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.