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.
Introduction
Great apes in zoos develop many chronic conditions that also are observed in
humans, chief among them being heart disease. As it is for human populations
around the globe, cardiac disease is the leading cause of death for captive
great apes (Baitchman et al., 2006; Lowenstine et al., 2016; McManamon and
Lowenstine, 2012; Murphy et al., 2018; Schmidt et al., 2006; Strong et al.,
2016, 2017, 2018; Varki et al., 2009; Videan et al., 2009), affecting
approximately 45 % of bonobos (Pan paniscus), 41 % of gorillas (Gorilla gorilla gorilla), 38 % of
chimpanzees (Pan troglodytes), and 20 % of orangutans (Pongo spp.) in North American zoos
(Lowenstine et al., 2016). Among gorillas, males are 8 times more likely
to die of heart disease than females (Strong et al. 2018); it is the cause
of death for 70 % of adult males (i.e., silverbacks) aged 30 years or
older (Lowenstine et al., 2016; McManamon and Lowenstine, 2012). Given its
prevalence, efforts continue to identify biological indicators that can
predict risk and monitor progression of cardiac disease in gorillas and
other great apes, such as leptin and adiposity (Dennis et al., 2019), blood
pressure (Junge et al., 1998; McManamon and Lowenstine, 2012; Murphy et al.,
2011, 2018), brain natriuretic peptide (BNP; McManamon and Lowenstine, 2012;
Murphy et al., 2018; Murray et al., 2019), and inflammatory cytokines (Edes
and Brand, 2020).
Humans are most commonly diagnosed with coronary artery disease (CAD), which
is characterized by the accumulation of atherosclerotic plaques within
arterial walls (Lowenstine et al., 2016; Strong et al., 2018; Varki et al.,
2009). As such, hyperlipidemia is a well-known risk factor for CAD in
humans, although half of all coronary events occur in people without it
(Libby, 2002; Willerson and Ridker, 2004). Given the similarities in disease
prevalence between humans and gorillas, it has generally been assumed that
the two species would share similar risk factors (e.g., Benirschke and
Adams, 1980; Junge et al., 1998). Seemingly consistent with this
expectation, zoo-housed gorillas have lipid profiles that appear
proatherogenic (i.e., might promote the growth of atherosclerotic plaques)
and would be considered hyperlipidemic if observed in humans (Baitchman et
al., 2006; Benirschke and Adams, 1980; Crissey et al., 1999; Kenny et al.,
1994; McGuire et al., 1989; Murphy et al., 2018; Popovich et al., 1997;
Schmidt et al., 2006; Varki et al., 2009). Furthermore, total cholesterol
and triglycerides are higher in captive than free-ranging gorillas (Schmidt
et al., 2006), who seem to rarely develop cardiac disease (Lowenstine et
al., 2016). Although atherosclerotic plaques (Cousins, 1972; Hruban et al.,
1986; Janssen and Bush, 1990; Kenny et al., 1994; McManamon and Lowenstine,
2012) and even CAD (Murphy et al., 2011) have been documented in a few
individuals, severe atherosclerosis in gorillas and other great apes is rare
(Lowenstine et al., 2016; McManamon and Lowenstine, 2012; Varki et al., 2009).
Instead, gorillas have primarily been diagnosed with fibrosing
cardiomyopathy (FCM), which is characterized by an increase in fibrous
connective tissue in the heart, resulting in reduced contractility and
conduction (Lowenstine et al., 2016; McManamon and Lowenstine, 2012; Murphy
et al., 2018; Strong et al., 2018). Similar conditions, also without
atherosclerosis, occur in humans (e.g., idiopathic myocardial fibrosis;
Lowenstine et al., 2016). The second most common form of cardiac disease in
captive western lowland gorillas is aortic dissection (Kenny et al., 1994;
Lowenstine et al., 2016; McManamon and Lowenstine, 2012; Murphy et al.,
2018; Strong et al., 2018), although it has only been documented in males
(Strong et al., 2018). While aortic dissections can occur along ruptured
plaques in humans, this was not observed in a study of aortic dissection in
eight gorillas even though six were reported to have atherosclerosis (Kenny
et al., 1994).
Given the low prevalence of atherosclerosis reported at necropsy, in one of
the first reviews on causes of disease and mortality in gorillas, Benirschke
and Adams (1980) reported that it was “of minor importance” to cardiac
disease risk. Citing this paper, some have extrapolated that there are no
correlations between lipid profiles and cardiac disease in gorillas (e.g.,
Baitchman et al., 2006; Junge et al., 1998), yet to our knowledge, there are
no published studies examining these relationships. Zoos frequently measure
lipid markers such as total cholesterol and triglycerides as part of routine
health monitoring in great apes and other species, although the data are
infrequently published, in part because of the availability of the
Species360 database (Species360, 2020) that is widely used by zoo
professionals and veterinarians and integrated into medical records
software. Means and standard deviations have been published for some of the
lipid markers included herein in captive gorillas (Table 1), with total
cholesterol being the most common. Of the few published studies, only one
analyzed age and sex associations, and the authors reported no significant
associations between total cholesterol and either variable (McGuire et al., 1989).
Descriptive statistics for lipid markers in western lowland
gorillas reported in previous studies and from the Species360 database.
TCTGHDLLDLapoA1TC/LDL/TG/(mmol/L)(mmol/L)(mmol/L)(mmol/L)(mg/dL)HDLHDLHDLSourcex‾8.70 alla McClure et al. (1972)8.59 Mn= 15, 5 males8.76 FSD1.26 all1.35 M1.21 Fmin6.36 all6.36 M6.72 Fmax11.77 all11.77 M11.74 Fx‾7.27a McGuire et al. (1989)SD0.31n= 59, sex distributionmin2.79not reportedmax10.91x‾a Junge et al. (1998)SDn=5, all malesmin5.02max7.91x‾6.251.272.153.702.911.720.59a,b Crissey et al. (1999)SD0.370.010.160.33n= 25, sex distributionminnot reportedmaxx‾6.781.462.153.972663.61.850.68b Baitchman et al. (2006)SD1.670.891.041.01761.0n= 15, 5 malesminmaxx‾6.61 M1.43 Ma Dennis et al. (2019)5.63 F1.83 Fn= 69, 44 malesSD1.65 M0.72 M1.71 F0.95 Fmin3.70 M0.44 M3.67 F0.75 Fmax9.75 M4.23 M9.90 F4.17 Fx‾6.41.32.222.880.59b Species360SDTC n= 1871 (403 animals)min0 (3.5)0 (0.4)0.3 (0.66)TG n= 1083 (300 animals)max13.9 (11.2)4.0 (3.1)6.53 (4.38)HDL n= 267 (125 animals)
a Reported TC, HDL, LDL, and TG values have been converted to mmol/L
(Rugge et al. 2011) for ease of comparison to values presented herein.
b Italicized lipoprotein ratios not reported, calculated here for
comparison to lipoprotein ratios presented herein.
c Values in parentheses are upper and lower boundaries of calculated
reference intervals (only values from serum collected during immobilizations
for routine veterinary examinations are included in reference intervals).
apoA1 – apolipoprotein A1; F – females; HDL – high-density lipoprotein; LDL
– low-density lipoprotein; M – males; TC – total cholesterol; TG –
triglycerides.
As biomarker thresholds are often used as an indicator of poor health (e.g.,
hypertension, hypoglycemia), setting diagnostic criteria first requires
knowledge of age- and sex-related patterns, yet there are a lack of even
basic biological data on lipids in gorillas. Therefore, we measured five
lipid markers from serum samples collected during immobilizations and
calculated three lipoprotein ratios to examine relationships with sex and
age as well as to determine if these physiological indicators predict risk
of all-cause morbidity, cardiac disease, and mortality in gorillas at three
North American zoos. We do want to emphasize that these data are presented
for comparative purposes and are not intended to define reference ranges or
healthy versus clinical levels of circulating lipid markers and lipoprotein
ratios, and as such any reported differences may not be clinically
meaningful.
MethodsSubjects
We obtained cryopreserved serum samples that had been collected during
immobilizations from western lowland gorillas (n= 61) currently or
previously housed at the Columbus Zoo and Aquarium (males: n= 10;
females: n= 16), Louisville Zoo (males: n= 9; females: n= 12), and
Omaha's Henry Doorly Zoo (males: n= 10; females: n= 4). One sample was
analyzed per gorilla. Males ranged in age from 7–52 years (n= 29,
x‾= 22.00, SD = 10.90) and females from 6–52 years (n= 32, x‾= 23.53, SD = 14.40). This study complied with
The Ohio State University Animal Care and Use (IACUC) protocols, was
approved by each participating institution, and adhered to the American
Society of Primatologists' Principles for the Ethical Treatment of Non-Human Primates.
Lipid markers and health data
Five lipid markers were assayed for each gorilla: apolipoprotein A1 (apoA1),
high-density lipoprotein (HDL), low-density lipoprotein (LDL), total
cholesterol (TC), and triglycerides (TGs). These data were also used to
calculate TC/HDL, LDL/HDL, and TG/HDL ratios. TG, TC, HDL, LDL, and apoA1
were quantitated on a RX Daytona automated clinical chemistry analyzer
(Randox Laboratories-US, Ltd., Kearneysville, WV, USA). Commercially available
reagents (TR3823, CH3810, CH3811, CH3841, and LP3838, respectively),
calibrators (CAL2351 (TG and TC), CH2673 (HDL and LDL), LP3023 (apoA1)), and
two-level controls (HN1530 and HE1532 (TG and TC), LE2661 and LE2663 (HDL,
LDL and apoA1)) were all purchased from Randox Laboratories-US, Ltd.
(Kearneysville, WV, USA). The technical ranges were 0–12.8 mmol/L, 0–17.0 mmol/L, 0.189–3.73 mmol/L, 0.189–22.2 mmol/L, and 6.5–233.0 mg/dL,
respectively. Serum was generally run neat or diluted 1:5 with saline
(SA3854) where necessary. The analyzer was subject to routine quality
control measurements throughout the study, with normal and elevated controls
for each analyte maintained within 2 standard deviations (SD) of the
respective target value. Diagnosed chronic conditions, including cardiac
disease, and age at death (if applicable) were recorded from zoo medical
records. Chronic conditions, cardiac disease, and mortality were coded as
dichotomous 0,1 variables (disease: 1 = present; mortality: 1 = deceased).
Quantitative analyses
Some studies have shown fluctuations in lipids and lipoproteins in response
to acute stressors in humans (Bachen et al., 2002; Dimsdale and Herd, 1982;
Niaura et al., 1992; Steptoe and Brydon, 2005; Stoney et al., 1988; van
Doornen et al., 1998), although results vary widely between individuals and
based on context (e.g., laboratory versus real-life stressors). Serum
samples from 47 of the 61 gorillas were obtained during immobilizations for
routine health exams. The other samples were collected during
immobilizations for clinical events (e.g., illnesses such as respiratory
infections, injuries requiring veterinary treatment, medical interventions
during parturition) rather than routine exams. Because these clinical events
could impact lipid and lipoprotein levels in gorillas the same way acute
stressors do in humans, we present results for the whole dataset as well as
for samples collected during routine immobilizations only (n= 47).
First, we examined effects of age and sex on serum lipid profiles. HDL and
apoA1 were normally distributed and analyzed using linear regression; TC,
TG, LDL, TC/HDL, LDL/HDL, and TG/HDL had a positive skew and were analyzed
using generalized linear models (GLMs) with a gamma distribution and a
log-link function. We then analyzed whether lipid markers predicted risk of
all-cause morbidity, cardiac disease, and mortality using GLMs with a
binomial distribution and a logit link. Our baseline model contained just
age and sex, as these variables independently associate with many health
outcomes. Eight additional models were constructed, each one containing age,
sex, and one lipid marker. For comparative purposes, we also analyzed
differences between gorillas with and without chronic conditions, both
altogether and cardiac disease only, using either linear regression or GLM
based on the distribution of each biomarker. Zoo ID was not included as a
random effect for any models, as it did not improve models and often
resulted in overfitting. Significance was set at α=0.05.
However, given the exploratory nature of this study, the small sample size,
and because statistical significance does not always indicate biological
importance, we also discuss results at p≤ 0.10 as nearing
significance.
While these quantitative methods are routinely used to analyze physiological
data, null hypothesis significance testing has been criticized frequently
(e.g., Greenland et al., 2016; Nakagawa and Cuthill, 2007; Smith, 2018) and
could be a problematic approach given that p values do not equate to
biological or clinical importance. One information theoretic approach that
has been proposed to overcome these issues in ecological studies is
multimodel inference. Therefore, we wanted to investigate multimodel
inference as an alternative method for identifying biomarkers that may be
useful in predicting and/or monitoring disease risk and progression. To do
this, we next analyzed the risk models described above as well as a global
model (age and sex plus all eight lipid markers) using multimodel inference
to determine which combination of variables best explain variation in each
health outcome (Grueber et al., 2011; Harrison et al., 2018; Symonds and
Moussalli, 2011). Multimodel inference uses Akaike's information criterion
(AIC/AICc for small sample sizes) and model weights, which is the
probability that a given model is the best of those included, to rank models
relative to one another. While there is some debate over criterion for a top
model set, one suggestion is models with ΔAICc < 2 are
equivalent to the top ranked model and that models with ΔAICc < 6 should not be discounted, especially in predictive contexts
(Arnold, 2010; Harrison et al., 2018; Richards, 2005, 2008; Richards et al.,
2011; Symonds and Moussalli, 2011). Therefore, we present the top model as
well as the top model set, which contains all models with ΔAICc < 6 and weights summing to ≥ 0.95 (Harrison et al., 2018). An
evidence ratio (ER) also is calculated for each model, which indicates the
likelihood of the top model being better than the current model (e.g.,
ER = 2.5 means the top model is approximately 2.5 times more likely to be
the best model than the model to which it is being compared; Symonds and
Moussalli, 2011). Finally, we calculated Nagelkerke's adjusted R2 as an
estimate of the variation in risk explained by each model.
Statistical analyses were conducted using R (v3.5.0, R Core Team 2018). GLMs
were analyzed using the “lme4” package (Bates et al., 2019). Multimodel
inference was conducted using “MuMin” (Bartoń, 2019), with
Nagelkerke's adjusted R2 estimated using “rcompanion” (Mangiafico,
2020). Visualizations were made with “ggplot2” (Wickham et al., 2020).
Results
More than half of the gorillas in this sample, 55.7 % (n= 34), were
diagnosed with a chronic condition of some kind, and 31.1 % (n= 19)
died between sample collection and data analysis. Cardiac disease was
diagnosed in 42.6 % (n= 26) of gorillas. Other diagnosed conditions
included arthritis, obesity, hypothyroidism, hypertension, cancer, and
periodontal disease; at least nine individuals (14.8 %) had comorbidities.
Males were more likely than females to have been diagnosed with a poor health outcome,
with 72.4 % (n= 21) having at least one chronic condition and 65.5 %
(n= 19) having cardiac disease; 31 % (n= 9) of males have died since
sample collection. Among females, 40.6 % (n= 13) had one or more
chronic conditions, 21.9 % (n= 7) had cardiac disease, and 31.3 % (n= 10) are now deceased.
Ranges, means, and standard deviations for each lipid marker for all samples
and for samples from routine immobilizations only, with both males and
females combined and separately by sex, are presented in Table 2.
Associations of sex (β± SE = 0.062 ± 0.066, p= 0.356) and
age (β± SE =-0.001± 0.003, p= 0.653) with cholesterol
were not significant. Males had significantly lower TG than females when all
values were retained in the dataset (β± SE =-0.405± 0.187,
p= 0.035, Fig. 1a) but not when only routine immobilizations were
included (β± SE =-0.189± 0.150, p= 0.214). Older
gorillas had significantly higher circulating levels of TG than younger
conspecifics (β± SE = 0.019 ± 0.007, p= 0.014, Fig. 2a).
HDL was significantly higher among males (β± SE = 0.576 ± 0.176, p= 0.002, Fig. 1b) and inversely associated with age (β± SE =-0.021± 0.007, p= 0.004, Fig. 2b). Associations of LDL
with sex (β± SE =-0.076± 0.108, p= 0.483) and age (β± SE = 0.001 ± 0.004, p= 0.895) were not significant.
Unlike HDL, apoA1 was not significantly different between males and females
(β± SE = 25.539 ± 15.734, p= 0.110) but showed the same
inverse association with age (β± SE =-1.772± 0.620, p= 0.006, Fig. 2c). When only samples from routine immobilizations were
included, apoA1 was no longer significantly associated with age (β± SE =-1.332± 0.685, p= 0.058). All three ratios were
significantly lower in males (TC/HDL: β± SE =-0.314± 0.122,
p= 0.013, Fig. 1c; LDL/HDL: β± SE =-0.414± 0.115, p= 0.001, Fig. 1d, TG/HDL: β± SE =-0.881± 0.387, p= 0.027, Fig. 1e) and were positively associated with age (TC/HDL: β± SE = 0.014 ± 0.005, p= 0.006, Fig. 2d; LDL/HDL: β± SE = 0.012 ± 0.005, p= 0.011, Fig. 2e; TG/HDL: β± SE = 0.038 ± 0.015, p= 0.015, Fig. 2f).
Descriptive statistics for total cholesterol, triglycerides, HDL, LDL, apolipoprotein A1, TC/HDL ratio, LDL/HDL ratio, and TG/HDL ratio in a
sample of zoo-housed western lowland gorillas.
All Females only nx‾SDminmaxnx‾SDminmaxAll valuesTC (mmol/L)616.341.633.8112.61326.141.514.1510.91TG (mmol/L)611.561.510.4811.50321.891.960.5311.50HDL (mmol/L)612.030.780.433.90321.740.640.433.13LDL (mmol/L)613.241.360.797.83323.361.530.797.83apoA1 (mg/dl)61203.0365.8850.87378.5632189.5960.2150.87315.74TC/HDL613.652.341.7618.77324.263.041.7618.77LDL/HDL611.820.970.515.14322.181.130.515.14TG/HDL611.333.430.1626.74321.964.650.2526.74Routine immobilizations only TC (mmol/L)476.371.653.9812.61256.021.474.1510.91TG (mmol/L)471.300.720.483.55251.400.830.533.55HDL (mmol/L)472.200.730.683.90251.890.550.683.13LDL (mmol/L)473.261.381.227.83253.351.551.227.83apoA1 (mg/dl)47218.3060.2092.73378.5625204.0453.5992.73315.74TC/HDL473.091.011.768.06253.421.231.768.06LDL/HDL471.610.740.513.95251.910.860.513.95TG/HDL470.740.770.164.65250.920.960.254.65Males only nx‾SDminmaxAll values TC (mmol/L)296.551.753.8112.61TG (mmol/L)291.190.610.483.00HDL (mmol/L)292.340.820.743.90LDL (mmol/L)293.111.161.807.60apoA1 (mg/dl)29217.8569.6778.71378.56TC/HDL292.980.761.895.15LDL/HDL291.430.550.743.46TG/HDL290.640.580.162.73Routine immobilizations only TC (mmol/L)226.771.793.9812.61TG (mmol/L)221.770.580.483.00HDL (mmol/L)222.560.761.183.90LDL (mmol/L)223.151.181.967.60apoA1 (mg/dl)22234.5164.33132.76378.56TC/HDL222.720.491.893.45LDL/HDL221.270.370.742.08TG/HDL220.540.420.161.99
In a sample of zoo-housed western lowland gorillas (n= 61),
males have significantly (a) lower TG (β± SE =-0.405± 0.187,
p= 0.035), (b) higher HDL (β± SE = 0.576 ± 0.176, p= 0.002), (c) lower TC/HDL ratio (β± SE = -0.314 ± 0.122, p= 0.013), (d) lower LDL/HDL ratio (β± SE = -0.414 ± 0.115, p= 0.001), and (e) lower TG/HDL ratio (β± SE =-0.881± 0.387, p=0.027; to improve visualization, figure shown without one outlier value
of 26.7, which does not change results) than females.
HDL – high-density lipoprotein; LDL – low-density lipoprotein; TC – total
cholesterol; TG – triglycerides.
In a sample of zoo-housed western lowland gorillas (n= 61),
older animals have significantly (a) higher TG (β± SE =0.019±0.007), (b) lower HDL (β± SE = -0.021 ± 0.007, p= 0.004), (c) lower apoA1 (β± SE = -1.772 ± 0.620, p= 0.006),
(d) higher TC/HDL ratio (β± SE = 0.014 ± 0.005, p= 0.006),
(e) higher LDL/HDL ratio (β± SE = 0.012 ± 0.005, p= 0.011),
and (f) higher TG/HDL ratio (β± SE = 0.038 ± 0.015, p= 0.015; to improve visualization, figure shown without one outlier value of
26.7, which does not change results) compared to younger conspecifics.
apoA1 – apolipoprotein A1; HDL – high-density lipoprotein; LDL – low-density
lipoprotein; TC – total cholesterol; TG – triglycerides.
We next analyzed how models containing sex, age, and individual lipid
markers predicted all-cause morbidity, cardiac disease, and mortality (Table 3). In the baseline model, all-cause morbidity risk was significantly higher
in male gorillas (β± SE = 1.988 ± 0.704, p= 0.005) and
increased with age (β± SE = 0.132 ± 0.037, p< 0.001). While sex and age remained significant predictors in nearly all
models containing sex, age, and one lipid marker, no individual lipid
markers were significantly associated with all-cause morbidity risk,
although LDL/HDL ratio neared statistical significance when only routine
immobilizations were considered (β± SE =-1.664± 0.958, p= 0.082). As with all-cause morbidity, cardiac disease risk was
significantly higher in males (β± SE = 2.175 ± 0.640, p= 0.001) and increased significantly with age (β± SE = 0.050 ± 0.025, p= 0.048). In models containing a lipid marker as well as sex and
age, only sex and age were significant predictors of cardiac disease risk.
Older age was significantly associated with increased mortality risk (β± SE = 0.062 ± 0.024, p= 0.008), but the effect of sex was
not significant (β± SE = 0.147 ± 0.599, p= 0.806). In
models containing lipid markers as well as sex and age, only age was
significantly associated with mortality risk.
Predictions of all-cause morbidity, cardiac disease, and
mortality risk in zoo-housed western lowland gorillas from generalized
linear models (GLMs) containing age and sex only as well as models
containing age, sex, and individual lipid markers. Bold values are significant at p≤0.05.
Ranges, means, and standard deviations, as well as results from GLMs between
lipid markers based on presence or absence of disease, are presented in
Tables 4 and 5. Only two analyses neared statistical significance (α< 0.10) for all-cause morbidity; TG/HDL was higher in females with
at least one chronic condition (β± SE = 1.086 ± 0.571, p= 0.067; Table 4), and there was an inverse association between TC and
all-cause morbidity in females when only routine immobilizations were
included (β± SE =-0.169± 0.095, p= 0.087; Table 4). When
all values were retained, females with cardiac disease had significantly
higher TG/HDL than females without (β± SE = 1.240 ± 0.557, p= 0.034; Table 5), but this relationship was no longer significant when
only routine immobilizations were included (β± SE =-0.619± 0.481, p= 0.211; Table 5). When only routine immobilizations were
analyzed, gorillas with cardiac disease neared significantly higher HDL (β± SE = 0.405 ± 0.210, p= 0.060; Table 5) and had
significantly lower TC/HDL (β± SE =-0.186± 0.089, p= 0.042; Table 5), LDL/HDL (β± SE =-0.269± 0.126, p= 0.038; Table 5), and TG/HDL (β± SE =-0.539± 0.261, p= 0.044; Table 5) ratios.
Differences in lipid markers and lipoprotein ratios in a sample
of zoo-housed western lowland gorillas based on presence or absence of
diagnosed chronic conditions, including cardiac disease.
Present Absent Analysis x‾SDminmaxx‾SDminmaxBSEpBoth sexes, all values retained (n=61) TCa6.321.743.8112.616.371.523.9810.91-0.0080.0670.906TGa1.681.880.4811.501.410.880.533.550.1710.2410.480HDLb2.010.800.433.702.050.780.703.90-0.0380.2040.853LDLa3.041.200.797.603.481.531.227.83-0.1350.1070.210apoA1b195.6071.2150.87378.56212.3858.46114.63344.29-16.7916.990.327TC/HDLa3.832.941.7618.773.431.252.047.660.1110.1610.493LDL/HDLa1.720.810.633.881.961.150.515.14-0.1290.1350.344TG/HDLa1.654.520.1926.740.930.960.163.990.5740.5570.307Both sexes, routine immobilizations only (n=47) TCb6.331.694.1512.616.431.643.9810.91-0.0160.0770.840TGa1.270.620.483.161.340.850.533.55-0.0540.1640.744HDLb2.240.710.683.702.150.771.183.900.0910.2170.677LDLb3.041.151.497.603.521.611.227.83-0.1460.1220.237apoA1b218.3161.3292.73378.56218.2960.29132.69344.290.02017.860.999TC/HDLa3.031.161.768.063.170.812.044.76-0.0430.0970.663LDL/HDLb1.470.630.633.601.790.850.513.95-0.1960.1310.143TG/HDLa0.720.840.194.650.780.690.162.73-0.0760.3110.808Males, all values retained (n=29) TCa6.601.853.8112.616.431.563.988.350.0250.1130.825TGa1.260.610.483.000.990.600.602.350.2390.2160.278HDLb2.300.750.743.702.451.031.183.90-0.1480.3450.672LDLa3.141.211.967.603.021.071.804.850.0390.1570.808apoA1b212.3569.3878.71378.56232.2773.03132.76344.29-19.9229.230.501TC/HDLa3.040.821.895.152.820.602.043.720.0750.1070.489LDL/HDLa1.470.590.773.461.330.430.741.900.1020.1600.528TG/HDLa0.670.580.192.730.550.600.161.990.2080.3870.596Males, routine immobilizations only (n=22) TCb6.821.854.7312.616.581.733.988.350.0370.1370.792TGa1.210.550.483.001.060.730.612.350.1390.2600.600HDLb2.500.631.583.702.771.161.183.90-0.2650.3910.505LDLb3.171.301.967.603.080.782.053.930.0290.1950.881apoA1b230.6259.48150.37378.56247.7685.40132.76344.29-17.1533.320.612TC/HDLa2.770.481.893.452.560.572.043.370.0790.0930.408LDL/HDLb1.280.350.772.081.250.480.741.790.0210.1510.892TG/HDLa0.520.270.191.200.600.780.161.99-0.1440.3780.707Females, all values retained (n=32) TCa5.861.494.159.416.341.544.8410.91-0.0780.0890.385TGa2.342.870.6911.501.590.930.533.550.3890.3230.238HDLb1.530.650.432.781.880.610.703.13-0.3430.2250.137LDLa2.891.200.795.623.681.671.227.83-0.2420.1580.136apoA1b168.5368.0850.87315.74204.0151.12114.63299.11-35.4821.050.102TC/HDLa5.104.451.7618.773.681.372.117.660.3260.2240.156LDL/HDLa2.120.960.633.882.221.260.515.14-0.0450.1890.814TG/HDLa3.237.160.2726.741.091.040.253.991.080.570.067Females, routine immobilizations only (n=25) TCb5.380.764.156.286.381.674.8410.91-0.1690.0950.087TGa1.370.770.693.161.420.880.533.55-0.0400.2500.871HDLb1.760.610.682.781.960.521.283.13-0.2020.2300.388LDLb2.810.821.493.823.661.791.227.83-0.2660.1790.151apoA1b195.0791.2092.73315.74209.0850.22132.69299.11-14.0122.620.542TC/HDLa3.531.821.768.063.360.802.114.760.0510.1500.739LDL/HDLb1.830.870.633.601.950.880.513.95-0.0680.1920.726TG/HDLa1.091.360.274.650.830.680.252.730.2740.4120.513
a Distribution of biomarker is left-skewed, differences analyzed using
generalized linear models with gamma distribution and log-links.
b Distribution of biomarker is normally distributed, differences
analyzed using linear regression.
TC, TG, HDL, and LDL reported in mmol/L; apoA1 reported in mg/dl.
apoA1 – apolipoprotein A1; HDL – high-density lipoprotein; LDL – low-density
lipoprotein; TC – total cholesterol; TG – triglycerides.
Differences in lipid markers and lipoprotein ratios in a sample
of zoo-housed western lowland gorillas based on presence or absence of
cardiac disease.
Present Absent Analysis x‾SDminmaxx‾SDminmaxBSEpBoth sexes, all values retained (n=61) TCa6.611.833.8112.616.141.453.9810.910.0740.0660.266TGa1.652.090.4811.501.490.890.533.550.1060.2440.666HDLb2.170.800.433.701.920.760.683.900.2540.2020.213LDLa3.181.320.797.603.281.401.227.83-0.0320.1100.772apoA1b206.7474.7050.87378.56200.2759.4992.73344.296.46917.180.708TC/HDLa3.743.241.7618.773.581.392.048.060.0440.1650.792LDL/HDLa1.620.760.633.881.971.090.515.14-0.1970.1340.147TG/HDLa1.695.140.1926.741.071.090.164.650.4600.5510.407Both sexes, routine immobilizations only (n=47) TCb6.591.804.1512.616.211.553.9810.910.0600.0770.441TGa1.180.540.483.001.380.830.533.55-0.1560.1610.339HDLb2.440.641.583.702.030.760.683.900.4050.2100.060LDLb3.211.251.497.603.291.491.227.83-0.0250.1260.842apoA1b231.8960.01150.37378.56208.2459.4492.73344.2923.6417.610.186TC/HDLa2.770.541.763.503.331.202.048.06-0.1860.0890.042LDL/HDLb1.370.480.632.271.790.850.513.95-0.2690.1260.038TG/HDLa0.530.280.191.200.900.970.164.65-0.5390.2610.044Males, all values retained (n=29) TCa6.651.913.8112.616.361.473.988.350.0450.1050.670TGa1.280.650.483.001.020.530.602.350.2240.2000.271HDLb2.320.790.743.702.390.911.183.90-0.0670.3250.838LDLa3.221.242.017.602.901.011.804.850.1060.1460.473apoA1b213.5472.9778.71378.56226.0365.87132.76344.29-12.4927.610.655TC/HDLa3.060.851.895.152.820.582.043.720.0810.1000.425LDL/HDLa1.500.610.773.461.280.400.741.900.1590.1470.290TG/HDLa0.690.610.192.730.540.530.161.990.2380.3590.513Males, routine immobilizations only (n=22) TCb6.931.924.7312.616.431.563.988.350.0740.1220.550TGa1.220.590.483.001.080.600.612.350.1300.2310.580HDLb2.550.661.583.702.591.001.183.90-0.0360.3560.920LDLb3.271.342.057.602.880.761.963.930.1280.1700.462apoA1b234.5662.38150.37378.56234.4373.54132.76344.290.13030.180.997TC/HDLa2.760.481.893.452.640.562.043.370.0450.0850.597LDL/HDLb1.300.360.772.081.220.410.741.790.0690.1350.618TG/HDLa0.520.290.191.200.580.640.161.99-0.1090.3480.757Females, all values retained (n=32) TCa6.481.734.159.416.051.474.3110.910.0700.1060.517TGa2.683.920.6911.501.670.940.533.550.4690.3530.194HDLb1.770.750.432.781.730.620.683.130.0370.2770.895LDLa3.071.630.795.623.441.521.227.83-0.1120.1980.574apoA1b188.2782.0450.87315.74189.9654.7692.73299.11-1.68926.170.949TC/HDLa5.596.001.7618.773.891.512.118.060.3640.2530.161LDL/HDLa1.941.070.633.882.251.160.515.14-0.1500.2240.506TG/HDLa4.409.860.2726.741.271.190.254.651.2400.5570.034Females, routine immobilizations only (n=25) TCb5.580.864.156.286.131.594.5510.91-0.0940.1220.448TGa1.060.400.691.561.490.890.533.55-0.3420.2820.238HDLb2.100.481.642.781.840.560.683.130.2590.2750.357LDLb3.021.031.493.823.441.661.227.83-0.1300.2310.580apoA1b223.8858.13168.79315.74199.0852.7992.73299.1124.8026.880.366TC/HDLa2.780.761.763.503.581.282.118.06-0.2520.1730.158LDL/HDLb1.570.770.632.271.990.880.513.95-0.2400.2250.298TG/HDLa0.550.290.270.951.021.050.254.65-0.6190.4810.211
a Distribution of biomarker is left-skewed, differences analyzed using
generalized linear models with gamma distribution and log-links.
b Distribution of biomarker is normally distributed, differences
analyzed using linear regression.
TC, TG, HDL, and LDL reported in mmol/L; apoA1 reported in mg/dl.
apoA1 – apolipoprotein A1; HDL – high-density lipoprotein; LDL – low-density
lipoprotein; TC – total cholesterol; TG – triglycerides.
When all values were retained for multimodel inference, the top model for
predicting risk of all-cause morbidity contained age, sex, and LDL, with a
22 % chance (wi=0.224) of it being the best model among those
tested. All models containing single lipid markers were within the top model
set and were within or near ΔAICc < 2 (Table 6). The top
model explained 53 % of the variation in morbidity risk (pseudo-R2= 0.527), with the remaining models in the top model set explaining
50 %–53 % (pseudo-R2 values range from 0.495–0.525). When only routine
immobilizations were included, the top model included age, sex, and LDL/HDL
ratio, with a 35 % chance of being the best model (wi=0.347). This
top model explained 57 % of the variation in risk of all-cause morbidity
(pseudo-R2=0.567). It was followed by the model containing age,
sex, and LDL (wi=0.169, pseudo-R2=0.543) and the model with
only age and sex (wi=0.143, pseudo-R2=0.496), both of which
were within ΔAICc < 2. All except the global model were
within the top model set for all-cause morbidity.
Results from multimodel inference analysis of which models with
age, sex, and lipid markers best explain all-cause morbidity risk in
zoo-housed western lowland gorillas. Models with weights summing to ≥
0.95 and Δ AICc < 6 are within the top model set; models in
italics are not contained within the top model set.
ModelKAICcΔAICcwiERR2All values retained (n=61) Age + sex + LDL461.90.000.224–0.527Age + sex362.10.130.2101.0670.495Age + sex +LDL/HDL462.10.190.2051.0930.525Age + sex + TC463.91.990.0832.6990.501Age + sex + apoA1464.12.180.0752.9870.498Age + sex +TG/HDL464.22.230.0683.2940.497Age + sex + TG464.32.400.0683.2940.495Age + sex +TC/HDL464.42.410.0673.3430.495Age + sex + HDL464.42.420.0673.3430.495Age+sex+all1093.931.960.000-0.279Routine immobilizations only (n=47) Age + sex +LDL/HDL447.70.000.347–0.567Age + sex + LDL449.11.440.1692.0530.543Age + sex349.41.770.1432.4270.496Age + sex +TC/HDL450.32.650.0923.7720.522Age + sex + TG450.52.850.0844.1310.519Age + sex +TG/HDL450.72.970.0734.7530.517Age + sex + TC450.73.000.0774.5060.516Age + sex + apoA1451.84.160.0438.0700.496Age + sex + HDL451.84.160.0438.0700.496Age+sex+all1071.123.470.000–0.456
AICc – Akaike's information criterion corrected for small sample size;
ΔAICc – difference between the model with the lowest AICc and the
current model; apoA1 – apolipoprotein A1; ER – evidence ratio, weight of the
model with the lowest AICc divided by the weight of the current model; HDL –
high-density lipoprotein; K – number of variables included; LDL – low-density
lipoprotein; R2 – coefficient of determination, Nagelkerke's
adjusted/pseudo-R2; TC – total cholesterol; TG – triglycerides;
wi – Akaike's weight, model probabilities.
For risk of cardiac disease, the top model contained age and sex only when
all values were retained, with a 32 % chance (wi=0.319) of being
the best model out of those tested. The rest of the top model set for
cardiac disease risk included all models with age, sex, and one lipid marker
(Table 7), and all models within the top model set were within or near to
ΔAICc < 2, suggesting equivalence with the top model.
Variance explained by models within the top model set ranged from 32 %–34 %
(pseudo-R2 values range from 0.319–0.335). When only routine
immobilizations were included, the top model contained age, sex, and TC/HDL
ratio (wi=0.333, pseudo-R2=0.431), followed by the models
with age, sex, and TG/HDL (wi=0.233, pseudo-R2=0.427) and
with age and sex only (wi=0.159, pseudo-R2=0.353). Like
all-cause morbidity, the global model was not included in the top model set.
Results from multimodel inference analysis of which models with
age, sex, and lipid markers best explain cardiac disease risk in zoo-housed
western lowland gorillas. Models with weights summing to ≥ 0.95 and
Δ AICc < 6 are within the top model set; models in italics
are not contained within the top model set.
ModelKAICcΔAICcwiERR2All values retained (n=61) Age + sex373.10.000.255–0.319Age + sex +TG/HDL474.21.060.1311.9470.339Age + sex + TC474.51.340.1301.9620.335Age + sex + TG474.61.460.1232.0730.333Age + sex +TC/HDL474.71.590.1152.2170.331Age + sex +LDL/HDL474.81.670.1112.2970.329Age + sex + HDL475.01.860.1002.5500.326Age + sex + apoA1475.32.210.0853.0000.320Age + sex + LDL475.42.290.0813.1480.319Age+sex+all1092.018.840.000–0.304Routine immobilizations only (n=47) Age + sex +TC/HDL454.90.000.333–0.431Age + sex +TG/HDL455.00.180.2331.4290.427Age + sex356.31.470.1592.0940.353Age + sex +LDL/HDL456.61.790.1362.4490.396Age + sex + TG457.42.520.0943.5430.381Age + sex + apoA1457.52.590.0913.6590.380Age + sex + HDL457.52.640.0893.7420.379Age + sex + TC458.73.840.0496.7960.354Age + sex + LDL458.73.860.0486.9380.353Age+sex+all1070.215.370.000–0.465
AICc – Akaike's information criterion corrected for small sample size;
ΔAICc – difference between the model with the lowest AICc and the
current model; apoA1 – apolipoprotein A1; ER – evidence ratio, weight of the
model with the lowest AICc divided by the weight of the current model; HDL –
high-density lipoprotein; K – number of variables included; LDL – low-density
lipoprotein; R2 – coefficient of determination, Nagelkerke's
adjusted/pseudo-R2; TC – total cholesterol; TG – triglycerides;
wi – Akaike's weight, model probabilities.
As with cardiac disease, mortality risk was best explained by a top model
with only age and sex when all values were retained (Table 8). The top model
was only 19 % (wi=0.190) likely to be the best model among those
included. Consistent with the two previous health outcomes examined, the top
model set contained all except the global model and, similar to cardiac
disease, these were all within or near ΔAICc < 2.
Interestingly, the top model explained the least amount of variation in
mortality risk (pseudo-R2=0.169), with the rest of the top model
set explaining 17 %–21 % (pseudo-R2 values range from 0.172–0.212).
Results for mortality risk did not change when only values from routine
immobilizations were included.
Results from multimodel inference analysis of which models with
age, sex, and lipid markers best explain mortality risk in zoo-housed
western lowland gorillas. Models with weights summing to ≥ 0.95 and
Δ AICc < 6 are within the top model set; models in italics
are not contained within the top model set.
ModelKAICcΔAICcwiERR2All values retained (n=61) Age + sex374.30.000.190–0.169Age + sex +TC/HDL474.40.140.1771.0730.212Age + sex +TG/HDL474.40.180.1481.2840.212Age + sex + TC474.60.370.1581.2030.208Age + sex + TG475.10.850.1241.5320.198Age + sex + LDL475.20.960.1181.6100.196Age + sex +LDL/HDL475.51.240.1021.8630.191Age + sex + apoA1476.42.130.0662.8790.173Age + sex + HDL476.42.170.0642.9690.172Age+sex+all1090.916.650.000-0.196Routine immobilizations only (n=47) Age + sex357.20.000.225–0.140Age + sex + LDL458.00.770.1541.4610.184Age + sex + TC458.20.960.1391.6190.179Age + sex + HDL458.71.490.1072.1030.164Age + sex + apoA1459.21.950.0852.6470.152Age + sex +LDL/HDL459.22.040.0812.7780.149Age + sex +TC/HDL459.52.280.0723.1250.143Age + sex +TG/HDL459.62.390.0683.3090.140Age + sex + TG459.62.390.0683.3090.140Age+sex+all1076.519.270.000–0.148
AICc – Akaike's information criterion corrected for small sample size;
ΔAICc – difference between the model with the lowest AICc and the
current model; apoA1 – apolipoprotein A1; ER – evidence ratio, weight of the
model with the lowest AICc divided by the weight of the current model; HDL –
high-density lipoprotein; K – number of variables included; LDL – low-density
lipoprotein; R2 – coefficient of determination, Nagelkerke's
adjusted/pseudo-R2; TC – total cholesterol; TG – triglycerides;
wi – Akaike's weight, model probabilities.
Discussion
Values reported herein for TC, TG, TC/HDL, LDL/HDL, and TG/HDL (when only
routine immobilizations are included) are consistent with data reported from
previous studies or in Species360 (ZIMS, 2019), while HDL, LDL, and apoA1
values reported in previous studies or in Species360 (ZIMS, 2019) were
higher than values reported in this dataset (Table 1). Older gorillas had
significantly higher TG, lower HDL, lower apoA1, and higher TC/HDL, LDL/HDL,
and TG/HDL ratios (Fig. 2). In humans, lipid dysregulation increases with
age, with increases in total cholesterol, triglycerides, LDL, and
lipoprotein ratios having been observed (for review, see Kreisberg and
Kasim, 1987). The lack of significant associations of age with TC and LDL in
gorillas may be due to their shorter average lifespans compared to humans,
as the increases in total cholesterol and LDL with age in the latter happen
gradually over multiple decades starting in the third decade of life
(Kresiberg and Kasim, 1987). We also observed multiple sex differences, with
significantly lower TG, TC/HDL, LDL/HDL, and TG/HDL as well as higher HDL in
males, although the relationship between TG and sex was no longer
significant when only routine immobilizations were included. We did not find
significant differences between males and females for TC or apoA1. Our
results are consistent with the only other report to look at age and sex
associations with total cholesterol in gorillas, which also showed no
significant associations with either variable (McGuire et al., 1989). Sex
differences in lipid markers also have been reported in humans. HDL is
higher in women than in men (Avogaro et al., 1978; Kang et al., 2011;
Kreisberg and Kasim, 1987; Russo et al., 2015; Seidell et al., 1991; Wang et
al., 2011), but contradictory sex differences have been reported for total
cholesterol (Isles et al., 1989; Kresiberg and Kasim, 1987; Seidell et al.,
1991; Weverling-Rijnsburger et al., 1997), triglycerides (Kreisberg and
Kasim, 1987; Langsted et al., 2011; Sarwar et al., 2007; Seidell et al.,
1991; Wang et al., 2011), LDL (Russo et al., 2015; Wang et al., 2011), and
TG/HDL (Gaziano et al., 1997; Kang et al., 2011).
Age and sex were the only significant predictors across models for all-cause
morbidity, cardiac disease, and mortality risk, although LDL/HDL ratio did
approach significance as a predictor of all-cause morbidity. This is
inconsistent with a large body of research in humans, which has repeatedly
shown that high levels of TC, LDL, and TG, and high TC/HDL, LDL/HDL, and
TG/HDL ratios, as well as low HDL and apoA1, are associated with increased
risk of cardiovascular-related morbidity and mortality (Amos et al., 1987;
Bittner et al., 2009; Castelli and Anderson, 1986; Chapman et al., 2011;
Chen et al., 2020; Corti et al., 1997; Damsgaard et al., 1990; da Luz et
al., 2008; Emerging Risk Factors Collaboration, 2009; Gaziano et al., 1997;
Hadaegh et al., 2009; Hokanson and Austin, 1996; Horwich et al., 2002; Isles
et al., 1989; Iso et al., 1989; Kannel et al., 1971; Kronmal et al., 1993;
Langsted et al., 2011; Lemieux et al., 2001; Libby, 2002; Manninen et al.,
1992; Millán et al., 2009; Nordestgaard et al., 2007; Ridker et al.,
2000, 2001; Sarwar et al., 2007; Schatz et al., 2001; Yunke et al., 2014).
Results were similar when we tested multimodel inference as an alternative
method for predicting risk, with top model sets for each health outcome
including all except the global model. Minimal differences between models
within top model sets suggest that while including individual lipid markers
alongside age and sex may help predict these health outcomes, they likely do
not improve predictions over using age and sex alone. The similarity of
results when using multimodel inference compared to more traditional
analyses supports the use of these information theoretic approaches instead
of, or alongside, null hypothesis significance testing.
The relationship between cardiac disease and LDL in humans is positive (Amos
et al., 1987; Emerging Risk Factors Collaboration, 2009; Koba et al., 2002;
Lemieux et al., 2001). Given that cardiac disease was by far the most
prevalent condition in this sample, the observation that LDL and LDL/HDL
ratio, which decreases as LDL declines, may be predictors of all-cause
morbidity but not cardiac disease specifically suggests LDL may play a role
in other disease processes. With both sexes combined and in females alone,
the relationship of LDL/HDL and LDL with all-cause morbidity was negative,
meaning gorillas with chronic conditions had lower values than gorillas
without. LDL has been observed to be lower in humans with arthritis (Liao et
al., 2014; Myasoedova et al., 2010). Arthritis was the second most prevalent
chronic condition in our sample, affecting 18 % of individuals (n= 11),
and may help explain these results. LDL may even play a causal role as, in
humans, those with genetically predicted elevations in LDL are at lower risk
for developing arthritis (Hindy et al., 2019). Arthritis has been documented
in both captive and wild great apes (Lowenstine et al., 2016), but common
risk factors have yet to be investigated. While arthritis is not a major
risk factor for mortality, many animals adaptively hide signs of pain or
weakness (Markowitz, 1982), which makes diagnosing such conditions difficult
for veterinarians. As such, identifying biomarkers to diagnose and monitor
arthritis may improve welfare in an aging captive great ape population
through the use of medication to reduce pain and inflammation in affected
individuals.
Few markers reached or approached significant differences based on presence
or absence of chronic conditions and/or cardiac disease. Females with at
least one chronic condition approached significantly higher TG/HDL when all
values were included and significantly lower TC when only routine
immobilizations were included. For cardiac disease, samples collected during
routine immobilizations showed higher HDL and lower TC/HDL, LDL/HDL, and
TG/HDL ratios in gorillas with cardiac disease, all of which are opposite of
what is observed in humans. These results are likely an artifact of the
differences between males and females that are observed for these variables,
as most are no longer significant when the sample is segregated by sex.
However, the difference between TG/HDL in females with and without cardiac
disease remains significant and shifts direction, with higher levels in
those affected; while this is consistent with human research, the effect is
no longer significant when only routine immobilizations are included.
Despite LDL/HDL and LDL being possible predictors of all-cause morbidity
based on our multimodel inference analyses, they were not significantly
different in gorillas based on the presence or absence of chronic
conditions. The minimal variation between lipids, lipoproteins, and
lipoprotein ratios for gorillas with and without cardiac disease likely
reflects differences in etiology. It has recently been suggested that a
genetic mutation, estimated to have occurred in a hominin ancestor 2–3
million years ago, may account for the pathological differences in cardiac
disease between humans and our closest relatives; loss of cytidine
monophosphate-N-acetylneuraminic acid (Neu5Ac) hydroxylase (CMAH) may
predispose humans to developing atherosclerotic plaques and CAD, whereas
other great apes primarily develop FCM (Kawanishi et al., 2019). However, we
again emphasize that it remains to be determined if any differences observed
herein are clinically relevant or could be used in any diagnostic capacity.
One limitation of these analyses is the lack of statistical power for
including intermediate models (i.e., models containing more than one lipid
marker) or interaction terms; small sample size may especially affect
multimodel inference analyses (Burnham and Anderson, 2002; Symonds and
Moussalli, 2011; Harrison et al., 2018). A second limitation is that while
our sample included geriatric females (aged 30–35 years and older), we lack
the hormonal and reproductive cycle data necessary to determine menopausal
status (Atsalis and Margulis, 2006; Margulis et al., 2007). Menopause can
affect circulating lipid levels in women (Treímollières et al., 1999) and
so could also play a role in female gorillas. Age cannot be used as a proxy
to examine menopausal differences because, as in women, there is
considerable variation in timing (Atsalis and Margulis, 2006; Margulis et
al., 2007). Given the complex relationship between cardiac disease and
metabolic syndrome, glucoregulatory parameters may be valuable in models
predicting disease and mortality risk. Glucose, insulin, and insulin
resistance were not independently associated with age, suggesting no loss of
glucoregulation in older individuals, or with cardiac disease risk in this
sample (data not shown). TG/HDL ratio has been used as a “surrogate
marker” of insulin resistance in humans (Bittner et al., 2009; Chen et al.,
2020; Cordero et al., 2009; Fan et al., 2011), but we observed no
significant associations with risk of cardiac disease or other health
outcomes in this sample. Research on lipid markers as predictors of disease
and mortality in gorillas would further benefit from longitudinal data, as
changes in lipid profiles over the lifespan may play a more important role
than any single cross-sectional measurement, especially regarding the
markers for which we observed significant associations with age. Finally,
although we tried to minimize the chances of our results being affected by
lipid responses to acute stressors by also analyzing data with
immobilizations for non-routine purposes excluded, immobilization alone has
been shown to result in increased cortisol in multiple primate species
(e.g., olive baboons: Sapolsky, 1982; chimpanzees: Anestis, 2009; Anestis et
al., 2006; Lambeth et al., 2006; Whitten et al., 1998; red colobus monkeys:
Wasserman et al., 2013; western lowland gorillas: Jacobs et al., 2014) and
could potentially trigger fluctuations in lipid markers even in the absence
of other stressors. Therefore, the fact that all animals included herein
were immobilized may partially explain the lack of differences observed
between the two. Furthermore, although it has been suggested that the stress
response during immobilization is due to disorientation prior to
unconsciousness, rather than the injection itself (Sapolsky, 1982), the
method of administration (i.e., hand-injection versus darting) likely
impacts perception of the event, which is known to influence physiological
responses (Everly and Lating, 2013; Maestripieri and Hoffman, 2011;
Ulrich-Lai and Herman, 2009).
Given the paucity of data on lipid markers and lipoprotein ratios in
zoo-housed western lowland gorillas, this report contributes to knowledge of
both their basic biology as well as factors that may or may not contribute
to disease and mortality risk. Our analyses indicate LDL, by itself or
within LDL/HDL ratios, may help predict risk of all-cause morbidity when
included in predictive models alongside age and sex, with an inverse
association observed between LDL and LDL/HDL with disease risk that may
reflect high levels of arthritis in the sample. However, using traditional
approaches, age and sex best predicted cardiac disease and mortality was
best predicted by age alone. When using multimodel inference, models with
individual lipid markers were alongside the baseline age and sex model in
the top model set, often within ΔAICc < 2. This suggests
that while lipid markers may potentially predict cardiac disease and
mortality risk in conjunction with age and sex, they are not better at doing
so than age and sex alone. Consistent with this suggestion, mean lipid
marker values were similar between gorillas irrespective of disease status,
with the few significant relationships observed likely being due to
differences between males and females. As such, while it remains to be
explored whether these results are clinically meaningful, individual lipid
markers are unlikely to contribute substantially to efforts to identify
biomarkers for predicting and monitoring cardiac disease risk in western
lowland gorillas. However, as age and sex fail to explain all observed
variability in disease and mortality risk, further investigation into
underlying factors is warranted.
Data availability
Data supporting the findings of this study are available on request from the corresponding author, ANE, and with agreement from the participating zoological institutions. Individually identifiable data are not publicly available due to confidentiality agreements made between the corresponding author, ANE, and each participating zoological institution.
Author contributions
ANE conceptualized the study and analyzed the data. ANE and KLE carried out
the lipid marker assays and interpreted results. JLB provided laboratory and
equipment support. ANE prepared the manuscript and revisions with
contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This research would not have been possible without the assistance of
veterinarians, keepers, and staff at the Columbus Zoo and Aquarium,
Louisville Zoo, and Omaha's Henry Doorly Zoo. The authors thank research
assistants Rebecca Makii, Michelle Forman, Balbine Jourdan, and Jessica
Schuster. The authors also thank Erin Kane and one anonymous referee for their helpful
feedback on improving this article.
Financial support
This research has been supported by the Endocrinology Research Laboratory at the Smithsonian Conservation Biology Institute, the Smithsonian Women's Committee, the Shared Earth Foundation, and Jan Sanders.
Review statement
This paper was edited by Gerhard Weinbauer and reviewed by Erin Kane and one anonymous referee.
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