Static magnets are widely marketed to the public with claims of effectiveness for relieving pain. One survey suggests that about 28 per cent of patients with rheumatoid arthritis, osteoarthritis or fibromyalgia, use magnets or copper bracelets for pain relief.

Do static magnets help reduce pain?

In this issue of CMAJ, Dr. Max H. Pittler and colleagues present the results of their meta-analysis of 9 randomized trials, all of which used a visual analogue scale to assess the difference between static magnets and placebo. The researchers found no effect of magnets on pain scores and conclude that the evidence does not support the use of static magnets for pain relief and, therefore, magnets cannot be recommended as an effective treatment.

However, the authors found that further investigation is needed into a possible benefit to people suffering from osteoarthritis.

This graphical abstract depicts the findings of Nie, Li, and Li et al., who estimate biological ages of organs and systems using 402 multiomics features from 4,066 individuals and demonstrate several applications. They find that organs and systems are aging at different rates, and biological ages could be utilized for population stratification, mortality prediction, and phenotypes of genetic association studies. CREDIT Nie, Li, and Li et al./Cell Reports

It’s common to say that someone looks either younger or older than their chronological age, but aging is more than skin deep. Our various organs and systems may have different ages, at least from a biological perspective. In a study published March 8 in the journal Cell Reports, an international team of investigators used biomarkers, statistical modeling, and other techniques to develop tools for measuring the biological ages of various organ systems. Based on their findings, the researchers report that there are multiple “clocks” within the body that vary widely based on factors including genetics and lifestyle in each individual.

“Our study used approaches that can help improve our understanding of aging and—more importantly—could be used some day in real healthcare practice,” says co-corresponding author Xun Xu of the Beijing Genomics Institute (BGI) and China National GeneBank (CNGB) in Shenzhen, China. “We used biomarkers that could be identified from blood and stool samples plus some measurements from a routine body checkup.”

The concept of evaluating people’s biological aging rates has been around since the 1970s, but earlier studies were focused either on developing methods for estimating one centralized aging index or studying the molecular aging biomarkers using tissues and cell cultures outside the body.

“There has been a lack of practical applications in a population-based sample for precisely estimating the aging rates of live people’s organs and systems,” says co-corresponding author Xiuqing Zhang, also of BGI and CNGB. “So we decided to design one.”

To do this research, the investigators recruited 4,066 volunteers living in the Shenzhen area to supply blood and stool samples and facial skin images and to undergo physical fitness examinations. The volunteers were between the ages of 20 and 45 years; 52% were female and 48% were male. “Most human aging studies have been conducted on older populations and in cohorts with a high incidence of chronic diseases,” says co-corresponding author Brian Kennedy of the National University of Singapore. “Because the aging process in young healthy adults is largely unknown and some studies have suggested that age-related changes could be detected in people as young as their 20s, we decided to focus on this age range.”

In total, 403 features were measured, including 74 metabolomic features, 34 clinical biochemistry features, 36 immune repertoire features, 15 body composition features, 8 physical fitness features, 10 electroencephalography features, 16 facial skin features, and 210 gut microbiome features. These features were then classified into nine categories, including cardiovascular-related, renal-related, liver-related, sex hormone, facial skin, nutrition/metabolism, immune-related, physical fitness-related, and gut microbiome features.

Because of the difference in sex-specific effects, the groups were divided into male and female. The investigators then developed an aging-rate index that could be used to correlate different bodily systems with each other. Based on their findings, they classified the volunteers either as aging faster or aging slower than their chronological age.

Overall, they discovered that biological ages of different organs and systems had diverse correlations, and not all were expected. Although healthy weight and high physical fitness levels were expected to have a positive impact, the investigators were surprised by other findings. For example, having a more diverse gut microbiota indicated a younger gut while at the same time having a negative impact on the aging of the kidneys, possibly because the diversity of species causes the kidneys to do more work.

The investigators also used their approach to look at other datasets, including the National Health and Nutrition Examination Survey from the US Center for Disease Control and Prevention and the Chinese Longitudinal Healthy Longevity Survey, which includes data on more than 2,000 centenarians with matched middle-aged controls. In addition, they looked at single nucleotide polymorphisms (SNPs) to determine whether differences could be explained by genetic factors. There, they did find certain pathways that could be associated with aging rates.

The researchers plan to regularly follow up with the study participants to track the development of aging and validate their findings. Future studies will use additional approaches for classifying features of aging and studying the interactions between organ systems.

They also plan to use single-cell technology to look at programmed aging in more detail. “It’s important to capture the cell-to-cell variation in an aging individual, as this will tell us important information about the heterogeneity within cell types and tissues and provide important insights into aging mechanisms,” says co-corresponding author Claudio Franceschi of Lobachevsky State University in Russia.

A well-established screening tool used to assess children for attention-deficit hyperactivity disorder (ADHD) may be less accurate when a child has an autism s). Pediatric researchers report that children with autism may mistakenly be diagnosed with ADHD because they have autism-related social impairments rather than problems with attention. This is important for understanding what are the right services and treatments for a child.

The study team, including one of the psychologists who developed the ADHD screening tool, concludes that the tool needs to be refined to better identify the correct condition , and that clinicians should supplement the screening tool with careful clinical interviews.

“One of our best current screening measures for ADHD may be over-diagnosing ADHD in children with autism,” said study leader Benjamin E. Yerys, PhD, a researcher in the Center for Autism Research at Children’s Hospital of Philadelphia (CHOP). “This is important because medications that work for ADHD may be less effective for a child on the autism spectrum.” Scale scores are used in recommending school-based services as well as treatment options.

The study, from researchers at CHOP, the Perelman School of Medicine at the University of Pennsylvania, and Baylor University, appeared online Oct. 13 in the Journal of Autism and Developmental Disorders.

One complicating factor is comorbidity–an estimated 30 percent or more of children with autiusm also have ADHD. The researchers suggest that the tool they analyzed, the ADHD Rating Scale Fourth Edition (ADHD-RS-IV), while well-validated in assessing ADHD in a general population of children, may overestimate ADHD in children with autism . The scale asks parents and teachers to provide numerical ratings in reply to 18 items about a child’s behavior: nine items on inattention and nine on hyperactivity and impulsivity.

Study co-author Thomas J. Power, PhD, director of CHOP’s Center for Management of ADHD, developed ADHD-RS-IV in the 1990s (An updated version, the fifth edition, was published earlier this year, but not used in the current study). “I’m excited to be involved in this study, and in efforts to refine our screening tools,” he said, “especially since few researchers have previously investigated using this scale in children with autism . Our research raises questions not only about this rating tool, but all such measures that rely on parent and teacher ratings to assess ADHD in children with autism .”

The current study analyzed ratings of 386 children, aged 7 to 17, who had autism without intellectual disability. To test whether the ADHD rating scale was valid in children on the autism spectrum, the research team used a procedure called factor analysis. They found that some questions on the ADHD rating scale were high for children with ASD instead of being high just for the subset of children who had significant ADHD symptoms.

“One underlying problem,” said Yerys, “may be in how we ask these questions.” For example, he explained, parents and teachers are asked “Does the child respond when spoken to directly?” However, supplying a yes or no answer to this question doesn’t distinguish between actual inattention (a symptom of ADHD) and a child’s lack of understanding about how to behave in a social situation in responding to another person–a social impairment often found in autism . Similarly, other questions focus on how well a child stays on task with play activity. ADHD may cause a child to be easily distracted from an activity, but another child may instead stop playing because of autism -related difficulties with social play.

The study team recommends modifying the rating scale to better minimize the influence of autism on ratings of target ADHD behaviors. Moreover, until a more nuanced rating scale is available, clinicians need to combine the ratings with a follow-up interview with parents to better understand whether a child’s behaviors stem from ADHD symptoms or social impairments.

Ideally, said Yerys, a clinician should be experienced in evaluating both ADHD and autism . Large pediatric facilities such as CHOP are particularly well positioned to provide such evaluations, having premier clinical and research centers in both conditions. Yerys added that, as precision medicine efforts expand, differentiating ADHD from autism will be essential in providing safer and more effective treatments based on a child’s specific condition.

“Until we’re able to develop and validate a new rating scale that takes symptoms of autism into account, parents who are concerned should seek out clinicians who are conducting evaluations for ADHD and are also taking into account the possibility of autism,” added Yerys.

Going forward, concluded Power, it will be important to strengthen local resources by improving training for community-based providers such as pediatricians and school psychologists in recognizing key distinctions in diagnosing and treating both ADHD and autism .

Millions of people throughout the world are affected by diabetes. In particular, the rise in the incidence of type 2 diabetes is associated with global increases in obesity and changes in diet. There is also a genetic component to the development of type 2 diabetes, and recent evidence suggests that the fetal environment can influence the onset of this disease. A new study in the Journal of Clinical Investigation suggests that a maternal diet low in protein predisposes offspring to type 2 diabetes. Ernesto Bernal-Mizrachi and colleagues at the University of Michigan fed female mice either a normal diet or one low in protein throughout their pregnancies.

Offspring of mothers fed a low protein diet had decreased insulin levels and fewer β cells, the insulin producing cells of the pancreas. Additionally, as adults, insulin secretion by β cells in these offspring was defective. The β cell dysfunction in the offspring of mothers fed a low protein diet was associated with altered expression of microRNAs and autophagy pathways. Importantly, activation of autophagy pathways in utero restored β cell function in offspring from low-diet fed mothers. This study provides insight into how a maternal diet that is low in protein diet alters offspring β cell mass and function, predisposing offspring to type 2 diabetes.

Data presented at EULAR 2012, the Annual Congress of the European League Against Rheumatism, demonstrates that tailoring biologic treatment to individual patients with rheumatoid arthritis (RA) can reduce total costs by €2,595,557 per 272 patients over 3 years (95 percentile range -€2,983,760 to -€2,211,755), whilst increasing effectiveness by an average of 3.67 quality-adjusted life years (QALYs)*. Cost savings were mostly on drug costs.

The Dutch study, which investigated 272 patients with RA starting adalimumab treatment, measured DAS28** HAQ*** and biologic use over three years. A treatment protocol for personalised care was defined in which EULAR**** response and adalimumab serum drug level test results at six months determined whether adalimumab treatment was continued or discontinued, dosing was altered or, in case of non-response, another biological treatment was started. Using a patient level Markov model, outcomes in DAS28, HAQ, and biologic use for the personalised care group were simulated and compared to the observed drug use and disease course.

Charlotte Krieckaert from the Jan van Breemen Research Institute, Reade, The Netherlands and lead author of the study stated: “Governments and health authorities around the world are looking to save money by cutting costs and providing reduced access to more expensive treatments. This study demonstrates that with careful monitoring and testing disease activity at six months, costs for RA treatment can be reduced and treatment effectiveness can actually increase.”

In total, €2,562,494 was saved on biological drug costs and testing costs amounted to €10,872, resulting in an average incremental cost-effectiveness ratio (ICER)***** of -€707,236 per QALY gained. Personalised care saved costs and was more effective in 77.6% of simulations, but was cost saving and less effective in 22.4%.