Testing lipid markers as predictors of all-cause morbidity, cardiac disease, and mortality risk in captive western lowland gorillas (<i>Gorilla gorilla gorilla</i>)

大猩猩 载脂蛋白B 内科学 医学 脂蛋白 高密度脂蛋白 胆固醇 生理学 疾病 内分泌学 风险因素 人口学 生物 古生物学 社会学
作者
Ashley N. Edes,Janine L. Brown,Katie L. Edwards
出处
期刊:Primate Biology [Copernicus GmbH]
卷期号:7 (2): 41-59 被引量:3
标识
DOI:10.5194/pb-7-41-2020
摘要

Abstract. Great apes and humans develop many of the same health conditions, including cardiac disease as a leading cause of death. In humans, lipid markers are strong predictors of morbidity and mortality risk. To determine if they similarly predict risk in gorillas, we measured five serum lipid markers and calculated three lipoprotein ratios from zoo-housed western lowland gorillas (aged 6–52 years, n=61, subset with routine immobilizations only: n=47): total cholesterol (TC), triglycerides (TGs), high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein A1 (apoA1), TC∕HDL, LDL∕HDL, and TG∕HDL. We examined each in relation to age and sex, then analyzed whether they predicted all-cause morbidity, cardiac disease, and mortality using generalized linear models (GLMs). Older age was significantly associated with higher TG, TC∕HDL, LDL∕HDL, and TG∕HDL, and lower HDL and apoA1. With all ages combined, compared to females, males had significantly lower TG, TC∕HDL, LDL∕HDL, and TG∕HDL, and higher HDL. Using GLMs, age, sex, and lower LDL∕HDL were significant predictors of all-cause morbidity; this is consistent with research demonstrating lower LDL in humans with arthritis, which was the second most prevalent condition in this sample. In contrast to humans, lipid markers were not better predictors of cardiac disease and mortality risk in gorillas, with cardiac disease best predicted by age and sex alone, and mortality risk only by age. Similar results were observed when multimodel inference was used as an alternative analysis strategy, suggesting it can be used in place of or in addition to traditional methods for predicting risk.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Jessie完成签到,获得积分10
1秒前
1秒前
2秒前
3秒前
didiaonn完成签到,获得积分10
3秒前
LWWW12完成签到,获得积分10
3秒前
eric888应助科研通管家采纳,获得10
3秒前
SJJ应助科研通管家采纳,获得10
3秒前
Jasper应助科研通管家采纳,获得10
3秒前
聪明凡之应助科研通管家采纳,获得10
3秒前
852应助科研通管家采纳,获得10
3秒前
王w应助科研通管家采纳,获得10
3秒前
香蕉诗蕊应助科研通管家采纳,获得10
3秒前
Orange应助科研通管家采纳,获得30
3秒前
王w应助科研通管家采纳,获得10
3秒前
科研通AI6应助科研通管家采纳,获得10
4秒前
李爱国应助科研通管家采纳,获得10
4秒前
eric888应助科研通管家采纳,获得10
4秒前
科研通AI6应助科研通管家采纳,获得10
4秒前
4秒前
4秒前
SJJ应助科研通管家采纳,获得10
4秒前
4秒前
yang完成签到,获得积分10
5秒前
Agu完成签到,获得积分10
6秒前
求助人员发布了新的文献求助10
7秒前
徐立涛发布了新的文献求助10
9秒前
www发布了新的文献求助10
9秒前
meili完成签到,获得积分10
12秒前
格拉希尔完成签到,获得积分10
12秒前
阔达的马里奥完成签到 ,获得积分10
14秒前
abcd_1067完成签到,获得积分10
14秒前
量子星尘发布了新的文献求助10
15秒前
诗梦完成签到,获得积分10
16秒前
姬鲁宁完成签到 ,获得积分10
17秒前
www完成签到,获得积分10
17秒前
17秒前
风趣秋白完成签到,获得积分0
18秒前
19秒前
CN1681681发布了新的文献求助10
21秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
人脑智能与人工智能 1000
King Tyrant 720
Silicon in Organic, Organometallic, and Polymer Chemistry 500
Principles of Plasma Discharges and Materials Processing, 3rd Edition 400
Pharmacology for Chemists: Drug Discovery in Context 400
El poder y la palabra: prensa y poder político en las dictaduras : el régimen de Franco ante la prensa y el periodismo 400
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5604083
求助须知:如何正确求助?哪些是违规求助? 4688908
关于积分的说明 14856973
捐赠科研通 4696430
什么是DOI,文献DOI怎么找? 2541128
邀请新用户注册赠送积分活动 1507314
关于科研通互助平台的介绍 1471851