New research finds that the placebo effect may be socially contagious. In other words, a doctor’s beliefs about whether or not a pain treatment will work can exert a subtle influence on how much pain the patient will actually experience. 

A doctor’s display of confidence in a treatment may make it more effective.

The power of placebo may extend beyond what researchers previously believed.

At first, they only used placebos as controls in drug experiments.

With time, however, placebos proved to have value as potential treatments in their own right.

Pain, depression, anxiety, irritable bowel syndrome, Parkinson’s disease, and epilepsy are only some of the conditions that placebos have shown promise in treating.

A new study has looked into another fascinating aspect of the placebo: Does it transmit socially, from one person to another? If so, how? More specifically, how does a doctor’s belief about the effects of a medication affect their patient’s experience of pain?

Laboratory at Dartmouth College in Hanover, NH — is the corresponding author of the new study.

Chang and colleagues have published their findings in the journal Nature Human Behaviour

Testing placebo power in 3 experiments

To study the phenomenon of socially transmitted placebo, the researchers devised three experiments. All three involved two different creams that were meant to relieve heat-induced pain by targeting pain receptors on the participants’ skin.

One of the creams was called thermedol, and the other was a control cream. Although different in appearance, both creams were actually placebos — namely, petroleum jelly with no pain relieving properties at all.

The researchers asked undergraduate students to play the roles of “doctors” and “patients.” They informed the “doctors” of the creams’ benefits and conditioned them to believe that thermedol was better at relieving pain than the control cream.

The first experiment consisted of 24 “doctor-patient” pairs. In each pair, the “patient” did not know which cream was thermedol and which was the control. Only the “doctor” knew which was the “effective” cream.

The researchers then applied the creams to the participants’ arms, followed by pain-inducing heat, in order to evaluate the effects of the cream. All participants received the same amount of heat.

During the experiment, all participants wore cameras that recorded their facial expressions in the doctor-patient interactions.

Using a machine-learning algorithm trained on facial signals of pain, the researchers were able to examine the effect of cues such as raised eyebrows, raised upper lips, or nose wrinkling on the perceived effectiveness of the treatments.

In this experiment, the participants reported experiencing less pain with thermedol, and skin conductance tests suggested that they actually did experience less discomfort. Their facial expressions also reflected less pain with thermedol.

In the other two experiments, the researchers applied the creams in different orders, and they led the doctors to believe that they were using thermedol when they were using the control creams, and vice versa.

The experimenters themselves were also blind to the study, not knowing which cream was which. In these experiments, the results were the same.

How doctors’ beliefs affect clinical results

Overall, across all three experiments, the results revealed that when the “doctors” believed that a treatment was effective, the “patients” reported feeling less pain. Their facial expressions and skin conductance tests also revealed fewer signs of pain.

The reasons for this remain unclear. However, the researchers believe that social contagion via facial cues is the most likely explanation.

“When the doctor thought that the treatment was going to work, the patient reported feeling that the doctor was more empathetic,” says Chang.

“The doctor may have come across as warmer or more attentive. Yet, we don’t know exactly what the doctor was doing differently to convey these beliefs that a treatment works. That’s the next thing that we’re going to explore,” he adds.

“What we do know though is that these expectations are not being conveyed verbally but through subtle social cues,” explains Chang.

“These findings demonstrate how subtle social interactions can impact clinical outcomes. You can imagine that in a real clinical context, if the healthcare providers seemed competent, empathetic, and confident that a treatment may work, the impact on patient outcomes could be even stronger.”

New blood test detects brain tumors with 87% accuracy

Dr. Matthew J. Baker, a reader in the Department of Pure and Applied Chemistry at the University of Strathclyde in Glasgow, United Kingdom, is the lead author of the new research.

He and his colleagues have now published their findings in the journal Nature Communications.

Of the study, Dr. Baker says, “This is the first publication of data from our clinical feasibility study, and it is the first demonstration that our blood test works in the clinic.”

Although it is quite rare, brain cancer often has a poor outlook.

According to the National Cancer Institute, around 0.6% of people will develop brain cancer or another cancer of the nervous system in their lifetime.

However, the 5 year survival rate for those who do receive such a diagnosis is less than 33%.

Largely, the poor outlook is due to the fact that brain tumors have very nonspecific symptoms, which makes them more difficult to distinguish from other conditions.

Study co-author Dr. Paul Brennan — a senior clinical lecturer and consultant neurosurgeon at the University of Edinburgh in the U.K. — explains, “Diagnosing brain tumors is difficult, leading to delays and frustration for lots of people.”

“The problem is that symptoms of brain tumor are quite nonspecific, such as headache, or memory problems. It can be difficult for doctors to tell which people are most likely to have a brain tumor,” he adds.

The lack of cost effective tests that can help doctors triage people with brain tumors in primary care also means that it takes longer to accurately diagnose brain cancer. This ultimately results in a poorer outlook.

The team’s new blood test brings much needed hope in this regard. Dr. Baker and colleagues used infrared light to create a “bio-signature” of people’s blood samples and applied artificial intelligence to scan for signs of cancer.

The test correctly identified brain cancer in a cohort of 104 people 87% of the time.

A more rapid means of diagnosis

As the researchers explain in their paper, they used a technique called attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy and coupled it with machine learning technology to detect brain cancer.

The authors explain that the technique is “a simple, label free, noninvasive, nondestructive” way of analyzing the biochemical profile of a blood sample without requiring extensive preparation of the sample.

The ATR-FTIR technique allowed the researchers to work out a biochemical “fingerprint” of brain cancer.

Dr. Baker and team trained a machine learning algorithm to use these biochemical fingerprints to diagnose brain cancer in a retrospective cohort of 724 people. This cohort included people with primary and secondary cancers as well as control participants without cancer.

They then used the algorithm to predict brain cancer cases in a sample of 104 participants. Of these, 12 people had cancer, including four cases of glioblastoma. This is one of the most aggressive forms of brain tumor.

The findings revealed a sensitivity of 83.3% and a specificity of 87% for the blood test. “With this new test, we have shown that we can help doctors quickly identify which [people] with these nonspecific symptoms should be prioritized for urgent brain imaging,” says Dr. Brennan.

“This,” he adds, “means a more rapid diagnosis for people with a brain tumor, and quicker access to treatment.”

Hayley Smith — an ambassador for the Brain Tumor Charity in Hampshire, U.K. — adds that it is “very encouraging to hear that this blood test can lead to a quicker diagnosis for brain cancer.”

“This kind of test will be vital to patients, helping people to get the correct diagnosis quicker, which ultimately will help people to get the urgent medical care that they need.”

A new study analyzing the data of more than 1,600 adults found that people with hypertension or type 2 diabetes had a higher risk of death from stroke or heart disease if they slept for less than 6 hours per night.

Type 2 diabetes and hypertension (high blood pressure) are two very common health conditions around the world.

While there are tried and true ways of managing them, these conditions can increase a person’s risk of developing heart disease and experiencing a stroke.

Recently, a study that featured in the Journal of the American Heart Association found that one factor — sleep — may play a significant role for people with these health conditions.

“Our study suggests that achieving normal sleep may be protective for some people with these health conditions and risks,” says lead author Julio Fernandez-Mendoza, Ph.D., from the Pennsylvania State College of Medicine in Hershey.

“However,” he cautions, “further research is needed to examine whether improving and increasing sleep through medical or behavioral therapies can reduce risk of early death.”

Is short sleep duration ‘a useful risk factor?’

In the new study, Fernandez-Mendoza and team analyzed the data of 1,654 participants — 52.5% of whom were women — between the ages of 20 and 74 years. All of the participants had enrolled in the Penn State Adult Cohort.

The researchers split the participants into two categories according to cardiometabolic risk. The participants in one group had stage 2 hypertension or type 2 diabetes, while those in the other group had received a diagnosis of or treatment for heart disease or stroke.

Moreover, the researchers had access to data regarding the participants’ sleep duration, as this cohort had agreed to a 1-night evaluation in a sleep laboratory between 1991 and 1998. They also had access to death records and associated documentation for the years from 1992 through to 2016.

The team’s analysis revealed that of the 512 people who had died by 2016, about two-fifths had died because of causes relating to heart disease or stroke, while close to one-quarter had died following a cancer diagnosis.

What caught the investigators’ attention was the fact that among individuals who had hypertension or type 2 diabetes, the risk of death due to heart disease or stroke was two times higher in those who slept for less than 6 hours per night than in those who slept for 6 hours or more.

For the individuals with one of these two health conditions who slept for longer, the increased risk of premature death was not significant.

Additionally, participants in the heart disease and stroke group who slept for less than 6 hours a night had almost three times the risk of dying from cancer-related causes.

“Short sleep duration should be included as a useful risk factor to predict the long term outcomes of people with these health conditions and as a target of primary and specialized clinical practices.

Julio Fernandez-Mendoza, Ph.D.

“I’d like to see policy changes so that sleep consultations and sleep studies become a more integral part of our healthcare systems. Better identification of people with specific sleep issues would potentially lead to improved prevention, more complete treatment approaches, better long term outcomes, and less healthcare usage,” suggests Fernandez-Mendoza.

While this research adds to the evidence that sleep plays a crucial role in the maintenance of health and well-being, the study authors do admit that their current analysis has some limitations.

The chief limitation, they note, is the fact that they only had access to data on the duration of a single night’s sleep.

As this data came from laboratory observations, they caution that there is a possibility that the participants’ normal sleep patterns may have been different than usual because they were in an unfamiliar environment.

“Nevertheless, the associations found for those other clustered non‐[cardiovascular and cerebrovascular disease] causes of death had the expected [hazard ratios] and provided confidence about the reliability and validity of our findings,” the authors argue in their study paper.