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ionophore It is expected that the incidence

It is expected that the incidence of ovary-driven conditions may vary considerably between populations depending upon duration of lifetime ovary exposure. Thus, we made no attempt here to estimate a ‘true’ incidence rate for mammary carcinoma or pyometra. Instead, we focused our efforts on estimating the relative lethality of these diseases. Our research strategy—the focused interrogation of a sample containing complete data on age at death—enabled us to determine age-anchored life expectancy and years of life lost per diagnosis. These two measures provide an estimate of the force of a disease on life-expectancy and premature mortality on a per case basis, which is not affected by incidence rate. Thus, if the incidence of mammary cancer or pyometra were 50% lower or two-fold higher, it ionophore would not have altered our estimates of the impact that each diagnosed case exerts on life expectancy or average years of life lost. The information the two measures provide are complementary. Age-anchored life expectancy estimates the average remaining years of life for each case, regardless of cause of death. Years of life lost per diagnosis describes the premature mortality attributable to case fatality, not case mortality due to other causes (Brown et al., 2009; Carter and Nguyen, 2012; Thiam et al., 2016). Years of life lost may offer valuable opportunities to model assumptions about important diseases (e.g., the impact of lowering case fatality, delaying age of disease onset, or varying the proportion of cases euthanased at the time of diagnosis), which might inspire fresh insights into the possible longevity consequences of current health practices and future interventions designed to reduce premature mortality (Burnet et al., 2005; Carter and Nguyen, 2012). Because the aim of this study was to critically evaluate the impact of age-related diseases on longevity, an age at death of 4 years was considered to be a reasonable lower boundary to study the longevity consequences of mammary cancer and pyometra. Conditions that may significantly contribute to early-life mortality—poisonings, road traffic accidents, neonatal infectious diseases, behavioral issues—were not the focus of this analysis. Although conditions associated with early-life mortality would contribute to the total years of life lost (i.e., the total burden of disease within a population), the calculation of the two measures used here to describe the longevity-shortening impact of mammary cancer and pyometra were not distorted by their exclusion.
Our openness to re-thinking the relationship between two ovary-driven diseases and longevity was a logical outgrowth of our work on the biology of exceptional longevity. In a previous study of 83 female Rottweilers who reached exceptional longevity—living at least 13 years, which represents more than 30% longer than average for this breed (Michell, 1999; Proschowsky et al., 2003; O’Neill et al., 2013)—keeping ovaries longer was associated with a longevity advantage (Waters et al., 2009). To determine whether this clue obtained from a group of Rottweilers with highly successful aging might be a biological signal operational in members of this breed with more typical longevity, we re-tested the association between years of ovary exposure and longevity in 242 females that lived up to 12.9 years (i.e., none of the females in this study sample reached exceptional longevity). Females with longer ovary exposure (≥4.3 years) had a statistically significant 17 months longevity advantage over females with shorter ovary exposure. Reconciling the notion that keeping ovaries longer increases the development of both mammary cancer and pyometra in this study population, but also promotes longevity might seem counterintuitive, even problematic. It is not. The concept of whole organism thinking predicts that any intervention—including the decision to remove or conserve ovaries—would be associated with biological trade-offs (Waters, 2014). Seen through the lens of whole organism thinking, it may be concluded that the beneficial effects of ovary conservation on longevity in this study cohort outweighed any detrimental effects. It should be noted that there are other considerations that figure into the decision by pet owners to pursue elective ovariohysterectomy that are not addressed by our study, such as limiting overpopulation of unwanted dogs, and other behavioral and quality of life issues. For certain, broader dialogues concerning optimal timing and techniques of sterilization are warranted. But results from focused studies such as this one can provide essential starting points to launch such dialogue. Our findings here that the two diseases considered to be the major health hazards of ovary conservation—mammary cancer and pyometra—are not associated with shortened longevity, situates use of whole organism thinking as all the more prescient as we take further steps toward understanding the physiological trade-offs provoked by elective endocrine organ removal.

ionophore br Statistical Analysis br Descriptive statistics are expressed as

Statistical Analysis

Descriptive statistics are expressed as mean ± standard deviation for continuous variables and as frequency and percentages for nominal variables. A paired t test was used to compare the excursion and peak motion speed between the right ionophore and the left diaphragm. The associations between the excursions of the diaphragms and participants\’ characteristics were evaluated by means of the Pearson\’s correlation coefficient and a simple linear regression or Student\’s t test depending on the type of variable (ie, continuous or nominal variable). Continuous variables were height, weight, BMI, tidal volume, vital capacity (VC, %VC), forced expiratory volume (FEV1, FEV1%, and %FEV1), and nominal variables were gender and smoking history. The robustness of the results of the univariate analyses was assessed with multiple linear regression models. The significance level for all tests was 5% (two sided). All data were analyzed using a commercially available software program (JMP; version 12, SAS, Cary, NC, USA).

Results

Participants\’ Characteristics

Table 1 shows the clinical characteristics of all the participants (n = 172).

Excursions and Peak Motion Speeds of the Bilateral Diaphragm

Univariate Analysis of Associations Between the Diaphragmatic Excursions and Participants\’ Demographics

Figure 3. Estimated regression line of the excursion of the diaphragm on BMI or tidal volume. (a) Association between BMI and excursion of the right diaphragm. (b) Association between BMI and excursion of the left diaphragm. (c) Association between tidal volume and excursion of the right diaphragm. (d) Association between tidal volume and excursion of the left diaphragm. Lines show estimated regression (a–d). All scatterplots show correlations (P < 0.05). BMI, body mass index.Figure optionsDownload full-size imageDownload high-quality image (226 K)Download as PowerPoint slide

Multivariate Analysis of Associations Between the Excursions and Participants\’ Demographics

Multiple linear regression analysis using all variables as factors (Model 1) demonstrated dna mutant weight, BMI, and tidal volume were independently associated with the bilateral excursion of the diaphragms (all P < 0.05) after adjusting for other clinical variables, including age, gender, smoking history, height, VC, %VC, FEV1, FEV1%, and %FEV1. There were no significant associations between the excursion of the diaphragms and variables including age, gender, smoking history, height, VC, %VC, FEV1, FEV1%, and %FEV1 (Table 4). Additionally, a multiple linear regression model using age, gender, BMI, tidal volume, VC, FEV1, and smoking history as factors (Model 2) was also fit as a sensitivity analysis, taking into account the correlation among variables (eg, BMI, height, and weight; VC and %VC; FEV1, FEV1%, and %FEV1). Model 2 (Supplementary Data S1) gave results consistent with Model 1 (Table 4): higher BMI and higher tidal volume were independently associated with the increased bilateral excursion of the diaphragms (all P < 0.05). The adjusted R2 in Model 1 was numerically higher than that in Model 2 (right, 0.19 vs. 0.16, respectively; left, 0.16 vs. 0.13, respectively).

br Image Analysis br The diaphragmatic motions on

Image Analysis

The diaphragmatic motions on sequential chest radiographs (dynamic image data) during tidal breathing were analyzed using prototype software (Konica Minolta, Inc.) installed in an independent workstation (Operating system: Windows 7 Pro SP1; Microsoft, Redmond WA; CPU: Intel Core i5-5200U, 2.20 GHz; memory 16 GB). The edges of the diaphragms on each dynamic chest radiograph were automatically determined by means of edge detection using a Prewitt Filter 18 ;  19. A board-certified radiologist with 14 years of experience in interpreting chest radiography selected the highest point of each ionophore as the point of interest on the radiograph of the resting end-expiratory position (Fig 2a). These points were automatically traced by the template-matching technique throughout the respiratory phase (Fig 2b, Supplementary Video S1), and the vertical excursions of the bilateral diaphragm were calculated (Fig 2c): the null point was set at the end of the expiratory phase, that is, the lowest point (0 mm) of the excursion on the graph is the highest point of each diaphragm at the resting end-expiratory position. Then the peak motion speed of each diaphragm was calculated during inspiration and expiration by the differential method (Fig 2c). If several respiratory cycles were involved in the 10 to 15-second examination time, the averages of the measurements were calculated.

Figure 2. Representative sequential chest radiographs and the graphs of excursion and peak motion of the diaphragms obtained by chest dynamic radiography (“dynamic X-ray phrenicography”). (a) Radiograph of the resting end-expiratory position. (b) Radiograph of the resting end-inspiratory position. (c) Graph showing the vertical excursions and the peak motion speeds of the bilateral diaphragm. A board-certified radiologist placed a point of interest (red point) on the highest point of each diaphragm on the radiograph at the resting end-expiratory position (a). These points were automatically traced by the template-matching technique throughout the respiratory phase (double arrows in b) (Supplementary Video S1); red double arrow indicates the vertical excursion of the right diaphragm and blue double arrow indicates that of the left diaphragm. Based on locations of the points on sequential radiographs, the vertical excursions and the peak motion speeds of the bilateral diaphragm were calculated (c). The lowest point (0 mm) of the excursion on the graph indicated that the highest point of each diaphragm was at the resting end-expiratory position (ie, null point was set at the end-expiratory phase) (c). (Color version of figure is available online.)Figure optionsDownload full-size imageDownload high-quality image (305 K)Download as PowerPoint slide

Pulmonary Function Tests

The pulmonary function tests were performed in all participants on the same day of the imaging study. Parameters of pulmonary function tests were measured according to the American Thoracic Society guidelines 20 ;  21 using a pulmonary function instrument with computer processing (DISCOM-21 FX, Chest MI Co, Tokyo, Japan).

Statistical Analysis

Descriptive statistics are expressed as mean ± standard deviation for continuous variables and as frequency and percentages for nominal variables. A paired t test was used to compare the excursion and peak motion speed between the right diaphragm and the left diaphragm. The associations between the excursions of the diaphragms and participants\’ characteristics were evaluated by means of the Pearson\’s correlation coefficient and a simple linear regression or Student\’s t test depending on the type of variable (ie, continuous or nominal variable). Continuous variables were height, weight, BMI, tidal volume, vital capacity (VC, %VC), forced expiratory volume (FEV1, FEV1%, and %FEV1), and nominal variables were gender and smoking history. The robustness of the results of the univariate analyses was assessed with multiple linear regression models. The significance level for all tests was 5% (two sided). All data were analyzed using a commercially available software program (JMP; version 12, SAS, Cary, NC, USA).