Dr Carolyn Lam: Welcome to Circulation on the Run, your weekly podcast summary and backstage pass to the journal and its editors. I'm Dr Carolyn Lam, associate editor from the National Heart Center and Duke National University of Singapore. Did you know that despite being one of the wealthiest nations in the world, the United States population has a shorter life expectancy compared to almost all other high-income countries in the world? Well, stay tuned to learn what Americans could do to narrow the life expectancy gap between the United States and other industrialized nations. Coming right up after these summaries.
Are microRNAs involved in nitrate tolerance? Well, the first original paper this week provides some answers. This is from co-corresponding authors Dr Bai and Zhang from Central South University in Changsha, China. Nitrate tolerance develops when there's dysfunction of the prostaglandin I2 synthase and prostaglandin I2 deficiency. These authors hypothesize that prostaglandin I2 synthase gene expression may be regulated by a microRNA-dependent mechanism in endothelial cells. They induce nitrovasodilator resistance by nitroglycerin infusion in Apoe deficient mice and studied endothelial function in both the mouse models as well as human umbilical vein endothelial cells. They found that nitric oxide donors induced atopic expression of microRNA 199a/b in endothelial cells, which was required for the nitrovasodilator resistance via repression of prostaglandin I2 synthase gene expression. Targeting this axis effectively improved nitrate tolerance. Thus, the atopic expression of microRNA 199 in endothelial cells induced by nitric oxide may explain prostaglandin I2 synthase deficiency in the progression of nitric tolerance. Thus, microRNA 199a/b may be a novel target for the treatment of nitric tolerance.
What are the long-term outcomes of childhood left ventricular noncompaction cardiomyopathy? Well, the next paper presents results from the National Population-Based Study in Australia. First author, Dr Shi, corresponding author, Dr Weintraub, from Royal Children's Hospital in Melbourne, looked at the National Australian Childhood Cardiomyopathy Study, which includes all children in Australia with primary cardiomyopathy diagnosed at less than 10 years of age between 1987 and 1996. Outcomes for left ventricular noncompaction patients with a dilated phenotype will compare to those with a dilated cardiomyopathy.
There were 29 patients with left ventricular noncompaction with a mean annual incidence of newly diagnosed cases of 0.11 per hundredth thousand at risks persons.
Congestive heart failure was initial symptom in 83%, and 93% had a dilated phenotype. The median age at diagnosis was 0.3 years of age. Freedom from death or transplantation was 48% at 10 years after diagnosis, and 45% at 15 years. Using propensity score inverse probability of treatment-weighted Cox regression, the authors found evidence that left ventricular noncompaction with a dilated phenotype was associated with a more than two-fold greater risk of death or transplantation.
The next paper reports the first application of multiomics and network medicine to calcific aortic valve disease. Co-first authors Dr Schlotter and Halu, corresponding author Dr Aikawa from Brigham and Woman's Hospital and Harvard Medical School in Boston, and their colleagues examined 25 human stenotic aortic valves obtained from valve replacement surgeries. They used multiple modalities, including transcriptomics and global unlabeled and label-based tandem-mass-tagged proteomics.
Segmentation of valves into disease stage–specific samples was guided by near-infrared molecular imaging. Anatomic-layer specificity was facilitated by laser capture microdissection. Side-specific cell cultures was subjected to multiple calcifying stimuli, and the calcification potential and basil or stimulated proteomics were evaluated. Furthermore, molecular interaction networks were built, and their central proteins and disease associations were identified.
The authors found that global transcriptional and protein expression signatures differed between the nondiseased, fibrotic, and calcific stages of calcific aortic valve disease. Anatomical aortic valve microlayers exhibited unique proteome profiles that were maintained throughout disease progression and identified glial fibrillary acidic protein as a specific marker of valvula interstitial cells from the spongiosa layer. In vitro, fibrosa-derived valvular interstitial cells demonstrated greater calcification potential than those from the ventricularis. Analysis of protein-protein interaction networks further found a significant closeness to multiple inflammatory and fibrotic diseases. This study is significant because it is the first application of spatially and temporarily resolved multiomics and network systems biology strategy to identify molecular regulatory networks in calcific aortic valve disease. It provides network medicine–based rational for putative utility of antifibrotic and anti-inflammatory therapies in the treatment of calcific aortic valve disease. It also sets a roadmap for the multiomic study of complex cardiovascular diseases.
The final paper tackles the controversy of antibiotic prophylaxis for the prevention of infective endocarditis during invasive dental procedures. This is from a population-based study in Taiwan. First author, Dr Chen, corresponding author, Dr Tu from Institute of Epidemiology and Preventive Medicine College of Public Health in National Taiwan University aimed to estimate the association between invasive dental treatments and infective endocarditis using the health insurance database in Taiwan.
They chose 2 case-only study designs. First a case-crossover, and second, self-controlled case series. Both designs used within-subject comparisons such that confounding factors were implicitly adjusted for. They found that invasive dental treatments did not appear to be associated with a larger risk of infective endocarditis in the short period following invasive dental treatment. Results were consistent from both study designs. The authors also did not find any association between invasive dental treatments and infective endocarditis even among the high-risk patients, such as those with a history of rheumatic disease or valve replacement.
In summary, these authors found no evidence to support antibiotic prophylaxis for the prevention of infective endocarditis before invasive dental treatments in the Taiwanese population. Whether antibiotic prophylaxis is necessary in other populations requires further study.
Alright, so that wraps it up for our summaries, now for our feature discussion.
The United States is one of the wealthiest nations worldwide, but Americans have a shorter life expectancy compared with almost all other high-income countries. In fact, the US ranks only 31st in the world for life expectancy at birth in 2015. What are the factors that contribute to premature mortality and life expectancy in the US? Well, today's feature paper gives us some answers. And I'm just delighted to have with us the corresponding author, Dr Frank Hu from Harvard T.H. Chan School of Public Health, as well as our dear associate editor, Dr Jarett Berry, from UT Southwestern.
Frank, could you begin by telling us a bit more about the inspiration for looking at this, what you did, and what you found?
Dr Frank Hu: So, we look at the impact of healthy lifestyle habits, life expectancy in the US as a nation. As you just mentioned, Americans have a shorter life expectancy compared with almost all other high-income countries, so in this study we wanted to estimate what kind of impact of lifestyle factors have, premeasured that and life expectancy in the US population.
What we did is to combine three datasets. One is our large cohort, Nurses’ Health Study, and Health Professionals Follow-Up Study. We use this large cohort to estimate the relationships between lifestyle habits and mortality. And the second data set we use is to get age and sex to specific mortality rates in the US as a nation. This is the CDC WONDER dataset. And the third dataset we used is the NHANES dataset, this is the National Health and Nutrition Examination Survey. We used this dataset to get the prevalence of healthy lifestyle factors in the general US as a nation. So, we used the three datasets to create age-specific, sex-specific life tables and estimated life expectancies.
At age 50, according to the number of healthy lifestyle habits that people would follow, what we found is that following several lifestyle factors can make a huge difference in life expectancies.
Here we talk about five basic lifestyle factors: not smoking, maintaining a healthy weight, exercise regularly—at least a half hour per day—and eating a healthy diet, and not drinking too much alcohol. No more than one drink per day for a woman, no more than two drinks per day for men. What we found is that, compared with people who did not adapt any of those low-risk habits, we estimated that the life expectancy at age 50 was 29 years for woman and about 26 years for men. But for people who adapted all five healthy lifestyle habits, life expectancy at age 50 was 43 years for women and 38 years for men. So, in other words, a woman who maintains all 5 healthy habits gained, on average, 14 years of life, and the men who did so gained 12 years life compared with those who didn't maintain healthy lifestyle habits. So I think this is a very important public health message. It means that following several bases of healthy factors can add substantial amount of life expectancy to the US population, and this could help to reduce the gap in life expectancy between the US population and other developed countries.
Dr Carolyn Lam: Thank you, Frank. You know that is such an important public health message that I am going to repeat it. Adhering to five lifestyle risk factors mainly, don't smoke, maintain a healthy weight, have regular physical activity, maintain a healthy diet, and have moderate alcohol consumption, AND a woman could increase her life expectancy at age 50 by 14 years and a man could do that by 12 years more. That is absolutely amazing.
Okay so Frank, actually, I do have a question though. These are remarkable datasets obviously, but they also go back to the 1980s. So did you see any chief risk factor that may have played more predominant apart with time?
Dr Frank Hu: We didn't specifically look at the changes in risk factors life expectancy, but among the five risk factors, not smoking is certainly the most important factor in terms of improving life expectancy. The good news is that prevalent smoking in the US has decreased substantially in the past several decades. However, the prevalence of other risk factors has actually increased. For example, the prevalence of obesity has increased two- or three-fold and the prevalence of regular exercise remained at a very low level, and also the diet quality in the US population is relatively poor. So, the combination of those risk factors have contributed to relatively low life expectancies in the US population.
Dr Carolyn Lam: Right. Obesity, not smoking, I hear you. I just wanted to point out to all the listeners too, you have to take a look at Figure 1 of this beautiful paper, it’s just so beautifully illustrated in it.
Jarett, you helped to manage and bring this paper through. What are your thoughts?
Dr Jarett Berry: Yeah, I just want to echo your comments, Carolyn, and Dr Hu. This is a fabulous paper, and a very important contribution characterizing these important associations in the US population. And I think, and the discussion thus far has been really helpful in putting all of this into context.
I do want to ask you, just a couple of, I guess more, philosophical questions about some of the observations in the paper. And one of them is the prevalence of the low-risk factor, those with a large number of low-risk factors, for example, in both the Nurses Health and in the Health Professional Follow-Up Study, you observed that the presence of five lifestyle factors was less than 2%. And it's interesting you see this in a large number of datasets and I think important, maybe for our readers to realize that there's two sides to the coin here.
One, the benefit of these low risk factors, but also, unfortunately, the low prevalence of these collections of healthy lifestyle factors that you've outlined.
Could you comment a little bit on that, and what that means, both maybe from a scientific point of view of perhaps, more importantly, from a public health stand point?
Dr Frank Hu: Yeah and this is very important observation and the number of people or the percentage of people who maintained all the five low-risk lifestyle habits is quite low in our cohort, even the nurses and health professionals, they are more health conscience in the general population. They have much better access to health care and also better access to healthy foods and have physical activity facilities. Despite all this potential advantages, and these more percentage of people who are able to maintain all five lifestyle risk factors.
On the other hand, about 10 to 15% of our participants did not adopt any of the five low-risk lifestyle habits. So it means that we still have a lot of work to do in terms of improving the lifestyle habits that we discussed earlier. The five risk lifestyle factors and in the general population, I think the percentage of people who adapt all the five lifestyle factors, probably even lower than 2%. And so that means that we have a huge public health challenge in front of us and have to improving the five lifestyle risk factors. One of the most important public health challenges as mentioned earlier is obesity because currently we have two-third of the US population is overweight or obese. So that's something I think is major public health challenges for us.
Dr Jarett Berry: Right, and it’s interesting looking at your Table 1, and those individuals who have all five low risk factors. It's interesting that the prevalence of physical activity was incredibly high. I have a great interest of impact of exercise on these types of outcomes and it's interesting that in both cohorts, six or seven hours a week of exercise was the mean physical activity level in those with five risk factors. So, it's interesting and in some ways, these lifestyle factors, they do tend to congregate or covary with one another such that those individuals who do spend that kind of time, albeit unfortunately more rare than we would like to see it, the increase in physical activity does tend to have a positive impact, not only on the weight, but also on healthy lifestyle or healthy diet choices.
Dr Frank Hu: Right, yeah this is a very good observation that what I do want to point out that our definition of regular exercise is pretty cerebral to put it in terms of the definition. So we define moderate to vigorous physical activity in our cohorts. We included not just running, playing sports, but it was also walking in a moderate intensity. So it means that people can incorporate physical activity into their daily life. For example, by walking from a train station and with climbing stairs in their workplace and so on and so forth. So here physical activity means both recreational activity and also moderate intensity activities such as graceful walking.
Dr Carolyn Lam: Frank, I think both of us listening are breathing a sigh of relief there and just for the listeners to understand too. These factors were dichotomized, right, and so you were describing the type of exercise and actually you used a three and a half hour per week limit to define healthy or not.
Similarly, just for reference the alcohol intake was 5 to 15g a day for women, or 5 to 30g a day for men. And normal weight was defined as a BMI of 18.5 to 24.9. I'm just thinking that if I were listening I'd want to know those cutoffs.
Now, can I ask a follow-up question, therefore to this dichotomy. As far as I understand you counted each of these risk factors equally, but did you try to do a weighted analysis by any chance? Did any one of them play a bigger role than others?
Dr Frank Hu: That's an interesting mathematical question because it’s very difficult to assign different weights to different risk factors because we look at, not just total mortality but also cardiovascular mortality and cancer mortality. So, you would have to use different weights for different causes of mortality. That would make the analysis much more complicated. But we did calculate a different type of score using five categories of each risk factor and then using that score, we were able to rank people in more categories so for that score the range is from five to 25, and we categorized people into quintiles or even more categories and the contrast in life expectancy between the lowest and the highest group is even greater. So, it means that, the higher number of healthy lifestyle factors, the greater life expectancy. Also, with each category, each lifestyle factors a high degree of adherence to that factor, the greater health benefit people will get. So, I think it's really accumulative fact of multiple risk factors and also the degree of adherence to each of the factors.
Dr Carolyn Lam: Again, such an important public health message.
Jarett, how do you think this is going to be received by the public at large?
Dr Jarett Berry: Very well received. I mean this is a very important observation demonstrating some of these disconcerting observations about life expectancy in the United States and as we think about strategies for improving the public health, I think Dr Hu's group has really helped us outline, very clearly, what other bodies such as the American Heart Association have been saying for years now, that lifestyle factors are so important in influencing cardiovascular risk, and in this case, life expectancy. It really does put, once again, the right amount of emphasis on the role these lifestyle factors of improving the public health. I think it’s going to be very well received and really helpful and important observation that all of us need to hear.
Dr Carolyn Lam: Listeners, don't forget this important message and tell your friends about it, please.
Thanks for joining us today, don't forget to join us again next week.