Perhaps the most profound advance in primate evolution occurred about 6 million years ago when our ancestors started walking on two legs. The gradual shift to bipedal locomotion is thought to have made primates more adaptable to diverse environments and freed their hands to make use of tools, which in turn accelerated cognitive development. With those changes, the stage was set for modern humans.

The genetic changes that made possible the transition from knuckle-based scampering in great apes to upright walking in humans have now been uncovered in a new study by researchers at Columbia University and the University of Texas.

Using a combination of deep learning (a form of artificial intelligence) and genome-wide association studies, the researchers have created the first map of the genomic regions responsible for skeletal changes in primates that led to upright walking. The map reveals that genes that underlie the anatomical transitions observed in the fossil record were strongly acted on by natural selection and gave early humans an evolutionary advantage.

“On a more practical level, we’ve also identified genetic variants and skeletal features that are associated with hip, knee, and back arthritis, the leading causes of adult disability in the United States,” says Tarjinder Singh, PhD, assistant professor of computational and statistical genomics (in psychiatry) at the Columbia University Vagelos College of Physicians and Surgeons and a co-leader of the study. 

For example, slight deviations from the average hip width-to-height ratio were associated with an increased risk of hip osteoarthritis, while slight deviations in the tibia-femur angle were associated with an increased risk of knee osteoarthritis. These insights could help researchers devise new ways to prevent and treat these debilitating conditions.

The findings were published July 21 in Science. The study was co-led by Vagheesh M. Narasimhan, PhD, assistant professor of integrative biology and of statistics and data sciences at the University of Texas at Austin.

New techniques deployed

The researchers applied deep learning to analyze more than 30,000 full-body X-rays from the UK Biobank. Deep learning, a technology modeled after the brain’s neural networks, trains computers to do what comes naturally to humans, such as driving a car or translating languages. In this case, the technique was used to standardize the X-rays, remove any images with quality issues, and then precisely measure dozens of skeletal features, tasks that would have taken the researchers months, if not years, to complete. 

Next, the researchers scanned the human genome to identify chromosomal regions associated with variations in 23 key skeletal measures, such as shoulder width, torso length, and tibia-to-femur angle. (These scans, called genome-wide association studies, involve surveying the genomes of large groups of people, looking for genomic variants that occur more frequently in those with a specific disease or trait compared to those without the disease or trait.) This process revealed 145 regions associated with genes that regulate skeletal development. Only a handful of these loci were known from previous studies.

Many of the 145 regions overlapped with “accelerated” regions of the human genome, which have rapidly evolved over eons compared with the same regions in great apes. In contrast, few genes associated with the heart, immune system, metabolism, and other traits were found in accelerated regions. 

“What we’re seeing is the first genomic evidence that there was selective pressure on genetic variants that affect skeletal proportions, enabling a transition from knuckle-based walking to bipedalism,” says Narasimhan.  

The study also shows the power of combining large-scale biobank data, machine learning, and genomics to help us understand human health and disease. Singh, who joined Columbia in 2022, is now applying these techniques to understand the causes of mental illness.

Anxiety and depression are the mental health issues most commonly associated with inflammatory arthritis, and it is well-established that there is a link between mental health issues and poor health outcomes.² The EULAR recommendations emphasize the need to assess mental health regularly;¹ however, little is known about the association between self-management and mental health in people with inflammatory arthritis.

At the 2023 EULAR congress, Vestergaard and colleagues report on their cross-sectional study in Denmark. This included 42,407 adult patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), or spondylarthritis (axSpA). The aim was to find out more about the association between low self-management behavior and people’s mental health status.

A total of 12,713 people responded to the questionnaire. The results showed the prevalence of anxiety was highest for patients with axSpA and lowest for RA: 34.5% compared to 22.1%. When looking at depression, the prevalence was highest for patients with PsA and lowest for RA: 27.2% compared to 18.6%. For both anxiety and depression, the prevalence was higher in women, younger patients under the age of 55, those with more newly diagnosed diseases (less than 3 years), and patients with basic education.

Patients with clinical anxiety and depression were more likely to have low self-management behaviour for all included self-management measures, such as adherence to treatment, physical activity, and taking an active role in their healthcare.

These findings call for a systematic approach to identifying mental health issues in patients with inflammatory arthritis.

Golf is acknowledged as a sport allowing players to blow off steam and enjoy the outdoors, but a new study led by the University of South Australia shows it may also have significant benefits for people with chronic disease osteoarthritis.

UniSA researcher Dr Brad Stenner from the Alliance for Research in Exercise, Nutrition and Activity (ARENA) and a team of academics from Australia and the UK found that golfers with the degenerative condition experience lower psychological distress and better general health compared to the general population.

The same was found with golfers without osteoarthritis.

The findings are reported in the Journal of Science and Medicine in Sport.

Osteoarthritis affects more than two million Australians who suffer joint pain and stiffness most commonly in their hands, neck, lower back, knees, or hips, contributing to a lower likelihood of meeting physical exercise guidelines.

Osteoarthritis is the most common form of arthritis, the leading cause of chronic pain and the second most common cause of disability.

In a survey of 459 golfers with osteoarthritis more than 90% of participants rated their health as good, very good or excellent, compared to just 64% of the general population with the condition.

Almost three times as many non-golfers (22%) reported high to very high levels of psychological distress compared to golfers with osteoarthritis (8%).

Dr Stenner, a lecturer and occupational therapist, says regular golfers are kept active due to the amount of walking required and they can also experience a range of social benefits.

“People who play golf are often walking 8-10km per round and, as such, are regularly meeting or exceeding recommended physical activity guidelines, which is known to reduce the risk of cardiovascular disease, diabetes, obesity and improve metabolic and respiratory health,” he says.

“There are also significant benefits to mental health and wellbeing.

“Our research has highlighted the important role that golf has in building friendships, contributing to community, and bringing a sense of belonging, all of which are known to contribute to mental health and wellbeing.”

Staying active and exercising regularly is one of the most important aspects of managing osteoarthritis.

“Lower impact activity such as golf can assist in maintaining activity whereas higher impact activities such as running, jogging and gym may place significant stress on the joints, contributing to increased symptoms and pain,” Dr Stenner says.

“There is a growing body of evidence that golf reduces the risk of many chronic conditions such as obesity, diabetes, and cardiovascular disease, and may contribute to the management of these illnesses, which in turn may lower the longer term health and medical costs.

“From a mental health point of view, playing golf is associated with improved wellbeing and lower levels of psychological distress, and this is an important consideration for older adults.”

Dr Stenner says there is a gap in the known literature on the topic despite it being one of the most popular sporting activities for older adults.

“Very little is known about the relationship between golf and health and there is so much more we need to find out,” he says.

A study by Swansea University has revealed how machine learning can help early detect Ankylosing Spondylitis (AS) inflammatory arthritis and revolutionise how people are detected and diagnosed by their GPs.

Published in the open-access journal PLOS ONE, the study, funded by UCB Pharma and Health and Care Research Wales, has been carried out by data analysts and researchers from the National Centre for Population Health & Wellbeing Research (NCPHWR).

The team used machine learning methods to develop a profile of the characteristics of people likely to be diagnosed with AS, the second most common cause of inflammatory arthritis. 

Machine learning, a type of artificial intelligence, is a method of data analysis that automates model building to improve performance and accuracy. Its algorithms build a model based on sample data to make predictions or decisions without being explicitly programmed to do so.

Using the Secure Anonymised Information Linkage (SAIL) Databank based at Swansea University Medical School, a national data repository allowing anonymised person-based data linkage across datasets, patients with AS were identified and matched with those with no record of a condition diagnosis.

The data was analysed separately for men and women, with a model developed using feature/variable selection and principal component analysis to build decision trees.

The findings revealed:

• In men, lower back pain, uveitis (inflammation of the eye’s middle layer), and non-steroidal anti-inflammatory drug (NSAID) use under age 20 are associated with AS development.
• Women showed an older age of symptom presentation compared to men with back pain and multiple pain relief medications.
• The test data had a good prediction rate of around 70%-80%; however, when applying the model to a general population, the team felt multiple models might be needed to narrow down the population over time to improve the predictive value and reduce the time to diagnose AS.