A study finds that human activity has led to a severe decrease in Caribbean shark abundance.
Image credit: Sean Mattson (International Center for Tropical Agriculture, Cali, Colombia).
Edited by Christopher W. Kuzawa, Northwestern University, Evanston, IL, and approved August 8, 2019 (received for review April 23, 2019)
Article Figures & SI
Figures
- Fig. 1.
Comparison of the LV structure and the function in chimpanzees and 2 representative human groups: sedentary Americans and Tarahumara subsistence farmers. (A–C) Scaled outlines of LVs among the 3 groups highlighting differences in trabeculation, WT, chamber size, and shape. (D) Basal and apical systolic (shaded) and diastole (unshaded) rotation. (E) Magnitude of LV twisting, untwisting, and the respective velocities during systole (shaded) and diastole (unshaded). While chimpanzees lack apical rotation and, thus, overall systolic twist, sedentary humans and subsistence farmers have similar levels of systolic twisting and early diastolic UTVs.
- Fig. 2.
Comparison of the LV structure and the function among chimpanzees and 4 human groups with diverse physical activity histories. (A and B) Principal component analysis of LV variables determined a priori to be associated with either pressure (A) or volume (B) exposure. Principal component scores are expressed as standardized Z scores. (C and D) General linear model of first principal component scores for pressure (C) and volume (D) regressed on group identity with means and 95% confidence intervals. Groups analyzed: chimpanzees (CHI); sedentary Americans (SAM); American-style football linemen (AFL); long-distance runners (LDR); and Tarahumara subsistence farmers (TAR). Pressure variables are as follows: peak systolic LV tissue velocity (S′), Mid-LV Mid RWT, basal-LV RWT, Mid-LV Mid WT, SBP, DBP; volume variables are early diastolic transmitral valve blood flow velocity (MVE), peak early diastolic tissue velocity (E′), SI, CO, SV, LVL, LV EDV, LV ESV, LV OT diameter. All ventricular structural data entered into the analyses were scaled as per SI Appendix.
- Fig. 3.
Trade-off between EPA and RPA training on the LV structure and function. (A) After 90 d of intensive training, the EPA athletes demonstrated eccentric LV remodeling characterized by increases in LV chamber volume (∆ EDV = 7%, ∆ RWT = −3%) and improved diastolic function (∆ E′ = 12%) while the RPA athletes demonstrated concentric remodeling characterized by (∆ WT = 13%, ∆ RWT = 8%) and reduced diastolic function (∆ E′ = −7%). (B) Relationship between RWT and LV SV in response to volume challenge (Top) and pressure challenge (Bottom) among these athletes after training. RPA-trained athletes with relatively thicker LV walls were less able than EPA-trained athletes to increase SV (∆SV = 6% vs. ∆SV = 16%) when challenged with rapid intravascular infusion of normal saline. In contrast, RPA-trained athletes were better able to preserve SV (∆SV = −4%) when pressure challenged with an isometric grip test than EPA-trained athletes (∆SV = −21%).
- Fig. 4.
Difference in SBP (solid line) and DBP (dashed line) in male chimpanzees, Tarahumara subsistence farmers, and sedentary Americans (NHANES data). Tarahumara subsistence farmers (blue) and sedentary Americans (green) are presented relative to age (shaded areas indicate 95% confidence interval of the mean), whereas a point estimate of mean BP relative to mean age is provided for the smaller chimpanzee cohort (red). Although BP increases with age in the NHANES sample, this is not apparent in the Tarahumara.
Tables
- Table 1.
Demographic, hemodynamic, and LV structure and function (scaled where appropriate) in semiwild sanctuary chimpanzees, Tarahumara, sedentary humans, LDRs, and American football linemen (AFL)
Parameter assessed Chimpanzeees (n = 43) All humans (n = 164) Tarahumara (n = 42) Sedentary humans (n = 40) LDRs (n = 42) AFL (n = 40) P value* Demographics Age (years) 21 ± 5 25 ± 8 33 ± 8† 27 ± 4† 20 ± 2 19 ± 1 0.015 Body Mass (kg) 55 ± 8 76 ± 18 62 ± 8† 75 ± 9† 67 ± 5† 102 ± 14† <0.001 Height (cm)‡ 127 ± 9 176 ± 10 164 ± 5† 176 ± 7† 179 ± 6† 185 ± 9† <0.001 Hemodynamics SBP (mmHg) 138 ± 21 116 ± 11 113 ± 11† 115 ± 8† 110 ± 8† 127 ± 9 <0.001 DBP (mmHg) 92 ± 17 67 ± 11 69 ± 10† 67 ± 8† 60 ± 8† 74 ± 13† <0.001 Heart rate/mass−0.25 158 ± 28 178 ± 31 165 ± 26 179 ± 24† 159 ± 24 209 ± 24† 0.002 CO (L/min)/mass0.75 0.147 ± 0.047 0.208 ± 0.049 0.181 ± 0.036† 0.188 ± 0.035† 0.250 ± 0.047† 0.210 ± 0.045† <0.001 LV structure Mid-LV WT (cm)/mass0.25 0.376 ± 0.089 0.307 ± 0.053 0.264 ± 0.033† 0.323 ± 0.039† 0.274 ± 0.026† 0.372 ± 0.024 <0.001 Mid-LV RWT 0.427 ± 0.113 0.366 ± 0.074 0.317 ± 0.058† 0.378 ± 0.050 0.322 ± 0.035† 0.452 ± 0.058 0.02 EDV (ml)/mass1.0 1.8 ± 0.3 2.0 ± 0.5 1.7 ± 0.3 1.7 ± 0.3 2.8 ± 0.3† 1.7 ± 0.2 0.201 ESV (ml)/mass1.0 0.91 ± 0.24 0.79 ± 0.29 0.63 ± 0.14† 0.63 ± 0.15† 1.20 ± 0.18† 0.68 ± 0.12† 0.095 LVL (cm)/mass0.25 2.80 ± 0.20 3.00 ± 0.29 3.11 ± 015† 2.79 ± 0.20 3.31 ± 0.20† 2.78 ± 0.16 <0.001 LV OT (cm)/mass0.25 0.720 ± 0.063 0.728 ± 0.055 0.755 ± 0.043 0.698 ± 0.046 0.768 ± 0.039† 0.688 ± 0.043 1 SI 1.68 ± 0.15 1.85 ± 0.17 1.90 ± 0.16† 1.84 ± 0.11† 1.93 ± 0.15† 1.72 ± 0.17 <0.001 LV Systolic function SV (ml)/mass1.0 0.926 ± 0.219 1.192 ± 0.304 1.109 ± 0.194† 1.057 ± 0.194 1.578 ± 0.205† 1.006 ± 0.197 <0.001 S′ (cm/s) 0.0624 ± 0.0134 0.1114 ± 0.0157 0.1015 ± 0.0157† 0.1143 ± 0.0093† 0.1067 ± 0.0146† 0.1235 ± 0.0133† <0.001 Peak LV twist (°)§ 6.4 ± 3.3 17.4 ± 6.2 18.0 ± 6.3† 18.4 ± 5.7† 14.4 ± 5.2† 16.5 ± 7.4† <0.001 LV diastolic function E (cm/s) 0.77 ± 0.17 0.83 ± 0.14 0.85 ± 0.20 0.84 ± 0.11 0.88 ± 0.13 0.77 ± 0.09 0.241 A (cm/s) 0.39 ± 0.15 0.51 ± 0.15 0.51 ± 0.15† 0.48 ± 0 0.11† 0.39 ± 0.09 0.65 ± 0.14† <0.001 E/A 2.21 ± 0.73 1.82 ± 0.69 1.79 ± 0.65 1.82 ± 0.44 2.38 ± 0.73 1.26 ± 0.38† 0.029 E′ (cm/s) 0.101 ± 0.024 0.136 ± 0.032 0.152 ± 0.030† 0.115 ± 0.014 0.168 ± 0.013† 0.107 ± 0.018 <0.001 A′ (cm/s) 0.062 ± 0.018 0.073 ± 0.025 0.102 ± 0.020† 0.051 ± 0.012 0.065 ± 0.011 0.074 ± 0.020 0.017 LV UTV (°/s)§ −53 ± 23 −109 ± 38 −109 ± 39† −121 ± 44† −109 ± 27† −96 ± 36† <0.001 SBP, systolic blood pressure; DBP, diastolic blood pressure; CO, cardiac output; LV, left ventricular; WT, wall thickness; RWT, relative WT; EDV, end diastolic volume; ESV, end systolic volume; OT, outflow tract; SV, stroke volume; S′, systolic ventricular wall velocity; LVL, LV length; E, early diastolic transmitral filling velocity; A, late diastolic transmitral filling velocity; E:A, the ratio of early to late diastolic transmitral filling velocities; E′, early diastolic ventricular wall velocity; A′, late diastolic ventricular wall velocity; UTV, untwisting velocity.
↵* Comparison of chimpanzees to all humans using 2-tailed unpaired Welch’s t tests for unequal variances. P values are adjusted for familywise error (37 tests) using the sequential Bonferroni method.
↵† P < 0.05 for comparison of chimpanzees to individual human groups using 2-tailed unpaired Welch’s t tests for unequal variances. P values are adjusted for familywise error (148 tests) using the sequential Bonferroni method.
↵‡ Calculated via the summation of lower limb and crown to rump measurements. n = 207 for all analyses except: EDV/mass1.0 (198); ESV/mass1.0 (198); SV/mass1.0 (198); CO/mass0.75 (198); sphericity index (SI) (204); MVE (206); MVA (206); MV E/A (206); mean E′ (206); mean A′ (206); mean S′ (205); twist peak (97); UTV (97). See SI Appendix for full table including all nonscaled variables.
↵§ For 2 angular variables (peak LV twist and LV UTV) a circular-linear model with a von Mises distribution for the response was used to compare means across groups.
Data supplements
Supporting Information
Selection of endurance capabilities and the trade-off between pressure and volume in the evolution of the human heart
Article Classifications
- Biological Sciences
- Anthropology
Sign up for Article Alerts
Jump to section
You May Also be Interested in
A study suggests that male dragonflies will evolve smaller melanin wing patches by 2070 as global warming continues.
Image credit: Pixabay/liggraphy.
The largest pandemic-related declines in nitrogen dioxide pollution occurred in the least White census tracts in the United States, which nonetheless faced higher levels during the pandemic than most White communities before the pandemic.
Image credit: Pixabay/SD-Pictures.
The approach can enable faster, more diverse study enrollments. But uncontrolled environments could also potentially lead to faulty data and flawed conclusions.
Image credit: Shutterstock/Andrey_Popov.
Genome editing technology has grown too quickly, and stakeholders in the debate are too diverse, for current approaches to establish a robust regulatory regime.
Image credit: Shutterstock/vchal.