Time series decomposition into dyslipidemia prevalence among urban Chinese population: secular and seasonal trends
Abstract Background Previous epidemiological studies have indicated the seasonal variability of serum lipid levels. However, little research has explicitly examined the separate secular and seasonal trends of dyslipidemia. The present study aimed to identify secular and seasonal trends for the preva...
Main Authors: | , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2021-09-01
|
Series: | Lipids in Health and Disease |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12944-021-01541-6 |
id |
doaj-f21a138983974efa972c6378fa08ac93 |
---|---|
record_format |
Article |
spelling |
doaj-f21a138983974efa972c6378fa08ac932021-09-26T11:52:15ZengBMCLipids in Health and Disease1476-511X2021-09-012011810.1186/s12944-021-01541-6Time series decomposition into dyslipidemia prevalence among urban Chinese population: secular and seasonal trendsJiahui Lao0Yafei Liu1Yang Yang2Peng Peng3Feifei Ma4Shuang Ji5Yujiao Chen6Fang Tang7Department of Endocrinology and Metabology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalDepartment of Endocrinology and Metabology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalDepartment of Endocrinology and Metabology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalSchool of Public Health, Weifang Medical UniversitySchool of Public Health, Weifang Medical UniversitySchool of Public Health, Weifang Medical UniversitySchool of Public Health, Weifang Medical UniversityDepartment of Endocrinology and Metabology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalAbstract Background Previous epidemiological studies have indicated the seasonal variability of serum lipid levels. However, little research has explicitly examined the separate secular and seasonal trends of dyslipidemia. The present study aimed to identify secular and seasonal trends for the prevalence of dyslipidemia and the 4 clinical classifications among the urban Chinese population by time series decomposition. Methods A total of 306,335 participants with metabolic-related indicators from January 2011 to December 2017 were recruited based on routine health check-up systems. Multivariate direct standardization was used to eliminate uneven distributions of the age, sex, and BMI of participants over time. Seasonal and trend decomposition using LOESS (STL decomposition) was performed to break dyslipidemia prevalence down into trend component, seasonal component and remainder component. Results A total of 21.52 % of participants were diagnosed with dyslipidemia, and significant differences in dyslipidemia and the 4 clinical classifications were observed by sex (P <0.001). The secular trends of dyslipidemia prevalence fluctuated in 2011–2017 with the lowest point in September 2016. The dyslipidemia prevalence from January to March and May to July was higher than the annual average (λ = 1.00, 1.16, 1.06, 1.01, 1.02, 1.03), with the highest point in February. Different seasonal trends were observed among the 4 clinical classifications. Compared to females, a higher point was observed among males in February, which was similar to participants aged < 55 years (vs. ≥ 55 years) and participants with a BMI ≤ 23.9 (vs. BMI > 23.9). Conclusions There were significant secular and seasonal features for dyslipidemia prevalence among the urban Chinese population. Different seasonal trends were found in the 4 clinical classifications of dyslipidemia. Precautionary measures should be implemented to control elevated dyslipidemia prevalence in specific seasons, especially in the winter and during traditional holidays.https://doi.org/10.1186/s12944-021-01541-6DyslipidemiaTime series decompositionSecular trendsSeasonal trendsHypercholesterolemiaHypertriglyceridemia |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jiahui Lao Yafei Liu Yang Yang Peng Peng Feifei Ma Shuang Ji Yujiao Chen Fang Tang |
spellingShingle |
Jiahui Lao Yafei Liu Yang Yang Peng Peng Feifei Ma Shuang Ji Yujiao Chen Fang Tang Time series decomposition into dyslipidemia prevalence among urban Chinese population: secular and seasonal trends Lipids in Health and Disease Dyslipidemia Time series decomposition Secular trends Seasonal trends Hypercholesterolemia Hypertriglyceridemia |
author_facet |
Jiahui Lao Yafei Liu Yang Yang Peng Peng Feifei Ma Shuang Ji Yujiao Chen Fang Tang |
author_sort |
Jiahui Lao |
title |
Time series decomposition into dyslipidemia prevalence among urban Chinese population: secular and seasonal trends |
title_short |
Time series decomposition into dyslipidemia prevalence among urban Chinese population: secular and seasonal trends |
title_full |
Time series decomposition into dyslipidemia prevalence among urban Chinese population: secular and seasonal trends |
title_fullStr |
Time series decomposition into dyslipidemia prevalence among urban Chinese population: secular and seasonal trends |
title_full_unstemmed |
Time series decomposition into dyslipidemia prevalence among urban Chinese population: secular and seasonal trends |
title_sort |
time series decomposition into dyslipidemia prevalence among urban chinese population: secular and seasonal trends |
publisher |
BMC |
series |
Lipids in Health and Disease |
issn |
1476-511X |
publishDate |
2021-09-01 |
description |
Abstract Background Previous epidemiological studies have indicated the seasonal variability of serum lipid levels. However, little research has explicitly examined the separate secular and seasonal trends of dyslipidemia. The present study aimed to identify secular and seasonal trends for the prevalence of dyslipidemia and the 4 clinical classifications among the urban Chinese population by time series decomposition. Methods A total of 306,335 participants with metabolic-related indicators from January 2011 to December 2017 were recruited based on routine health check-up systems. Multivariate direct standardization was used to eliminate uneven distributions of the age, sex, and BMI of participants over time. Seasonal and trend decomposition using LOESS (STL decomposition) was performed to break dyslipidemia prevalence down into trend component, seasonal component and remainder component. Results A total of 21.52 % of participants were diagnosed with dyslipidemia, and significant differences in dyslipidemia and the 4 clinical classifications were observed by sex (P <0.001). The secular trends of dyslipidemia prevalence fluctuated in 2011–2017 with the lowest point in September 2016. The dyslipidemia prevalence from January to March and May to July was higher than the annual average (λ = 1.00, 1.16, 1.06, 1.01, 1.02, 1.03), with the highest point in February. Different seasonal trends were observed among the 4 clinical classifications. Compared to females, a higher point was observed among males in February, which was similar to participants aged < 55 years (vs. ≥ 55 years) and participants with a BMI ≤ 23.9 (vs. BMI > 23.9). Conclusions There were significant secular and seasonal features for dyslipidemia prevalence among the urban Chinese population. Different seasonal trends were found in the 4 clinical classifications of dyslipidemia. Precautionary measures should be implemented to control elevated dyslipidemia prevalence in specific seasons, especially in the winter and during traditional holidays. |
topic |
Dyslipidemia Time series decomposition Secular trends Seasonal trends Hypercholesterolemia Hypertriglyceridemia |
url |
https://doi.org/10.1186/s12944-021-01541-6 |
work_keys_str_mv |
AT jiahuilao timeseriesdecompositionintodyslipidemiaprevalenceamongurbanchinesepopulationsecularandseasonaltrends AT yafeiliu timeseriesdecompositionintodyslipidemiaprevalenceamongurbanchinesepopulationsecularandseasonaltrends AT yangyang timeseriesdecompositionintodyslipidemiaprevalenceamongurbanchinesepopulationsecularandseasonaltrends AT pengpeng timeseriesdecompositionintodyslipidemiaprevalenceamongurbanchinesepopulationsecularandseasonaltrends AT feifeima timeseriesdecompositionintodyslipidemiaprevalenceamongurbanchinesepopulationsecularandseasonaltrends AT shuangji timeseriesdecompositionintodyslipidemiaprevalenceamongurbanchinesepopulationsecularandseasonaltrends AT yujiaochen timeseriesdecompositionintodyslipidemiaprevalenceamongurbanchinesepopulationsecularandseasonaltrends AT fangtang timeseriesdecompositionintodyslipidemiaprevalenceamongurbanchinesepopulationsecularandseasonaltrends |
_version_ |
1716867744943570944 |