Components of excitation-contraction (E-C) coupling were compared in ventricular myocytes isolated from 3-mo-old male and female rats. Ca(2+) concentrations (fura-2) and cell shortening (edge detector) were measured simultaneously (37 degrees C). Membrane potential and ionic currents were measured with microelectrodes. Action potentials were similar in male and female myocytes, but contractions were smaller and slower in females. In voltage-clamped cells, peak contractions were smaller in females than in males (5.1 +/- 0.7% vs. 7.7 +/- 0.8% diastolic length, P < 0.05). Similarly, Ca(2+) transients were smaller in females than in males and the rate of rise of the Ca(2+) transient was slower in females. Despite smaller contractions and Ca(2+) transients in females, Ca(2+) current density was similar in both groups. Sarcoplasmic reticulum Ca(2+) content, assessed with caffeine, did not differ between the sexes. However, E-C coupling gain (rate of Ca(2+) release/Ca(2+) current) was smaller in females than in males (157.0 +/- 15.6 vs. 338.4 +/- 54.3 (nM/s)/(pA/pF), P < 0.05). To determine whether the reduced gain in female cells was due to changes in unitary Ca(2+) release, spontaneous Ca(2+) sparks were evaluated (fluo-4, 37 degrees C). Spark frequencies and widths were similar in both groups, but spark amplitudes were smaller in females than in males (0.56 +/- 0.01 vs. 0.64 +/- 0.01 DeltaF/F(0), P < 0.05). Spark durations also were shorter in females than in males (full duration at half-maximum = 14.86 +/- 0.17 vs. 16.25 +/- 0.27 ms, P < 0.05). These observations suggest that decreases in the size and duration of Ca(2+) sparks contributes to the decrease in E-C coupling gain in female myocytes. Thus, differences in cardiac contractile function arise, in part, from differences in unitary Ca(2+) release between the sexes.

When people age their mortality rate increases exponentially, following Gompertz's law. Even so, individuals do not die from old age. Instead, they accumulate age-related illnesses and conditions and so become increasingly vulnerable to death from various external and internal stressors. As a measure of such vulnerability, frailty can be quantified using the frailty index (FI). Larger values of the FI are strongly associated with mortality and other adverse health outcomes. This association, and the insensitivity of the FI to the particular health variables that are included in its construction, makes it a powerful, convenient, and increasingly popular integrative health measure. Still, little is known about why the FI works so well. Our group has recently developed a theoretical network model of health deficits to better understand how changes in health are captured by the FI. In our model, health-related variables are represented by the nodes of a complex network. The network has a scale-free shape or "topology": a few nodes have many connections with other nodes, whereas most nodes have few connections. These nodes can be in two states, either damaged or undamaged. Transitions between damaged and non-damaged states are governed by the stochastic environment of individual nodes. Changes in the degree of damage of connected nodes change the local environment and make further damage more likely. Our model shows how age-dependent acceleration of the FI and of mortality emerges, even without specifying an age-damage relationship or any other time-dependent parameter. We have also used our model to assess how informative individual deficits are with respect to mortality. We find that the information is larger for nodes that are well connected than for nodes that are not. The model supports the idea that aging occurs as an emergent phenomenon, and not as a result of age-specific programming. Instead, aging reflects how damage propagates through a complex network of interconnected elements.

Aging is associated with the accumulation of damage throughout a persons life. Individual health can be assessed by the Frailty Index (FI). The FI is calculated simply as the proportion f of accumulated age-related deficits relative to the total, leading to a theoretical maximum of f≤1. Observational studies have generally reported a much more stringent bound, with f≤f_{max}<1. The value of f_{max} in observational studies appears to be nonuniversal, but f_{max}≈0.7 is often reported. A previously developed network model of individual aging was unable to recover f_{max}<1 while retaining the other observed phenomenology of increasing f and mortality rates with age. We have developed a computationally accelerated network model that also allows us to tune the scale-free network exponent α. The network exponent α significantly affects the growth of mortality rates with age. However, we are only able to recover f_{max} by also introducing a deficit sensitivity parameter 1-q, which is equivalent to a false-negative rate q. Our value of q=0.3 is comparable to finite sensitivities of age-related deficits with respect to mortality that are often reported in the literature. In light of nonzero q, we use mutual information I to provide a nonparametric measure of the predictive value of the FI with respect to individual mortality. We find that I is only modestly degraded by q<1, and this degradation is mitigated when increasing number of deficits are included in the FI. We also find that the information spectrum, i.e., the mutual information of individual deficits versus connectivity, has an approximately power-law dependence that depends on the network exponent α. Mutual information I is therefore a useful tool for characterizing the network topology of aging populations.

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