CRTC1 may play role in weight regulation.

Researchers found a link between genetic polymorphisms of CRTC1  and differences in BMIs in psychiatric patients, with a weaker association observed in the general population, according to data reported in JAMA Psychiatry.

“The genetic influence on obesity appears much stronger in psychiatric populations than in the general population, possibly because of the high prevalence of illness- and/or pharmacological treatment-related obesity in the former populations,” Chin B. Eap, PhD, one of the study authors and professor of pharmacogenetics and psychopharmacology at the Faculty of biology and medicine of the University of Lausanne, told Endocrine Today.


Previous research in mice has suggested the CREB-regulated transcription coactivator 1 (CRTC1) is associated with hypothalamic control of food intake, and low energy expenditure were observed in mice without the gene.

Eap and colleagues retrospectively assessed data from a Swiss cohort of 152 psychiatry patients (sample 1) taking weight gain-inducing psychotropic drugs. The TaqMan allelic discrimination assay (Applied Biosystems) was used for patient genotyping.

Data indicate that rs3746266A>G, a CRTC1 polymorphism, was significantly associated with BMI in this cohort (P=.003). The difference in BMI between patients with the G allele and in non-carriers was 2.13 kg/m(95% CI, 0.62-3.49; P=.001).

Researchers replicated this variant in two independent psychiatric samples (sample 2, n=174; sample 3, n=118) and two white population-based samples, they wrote.

Samples 2 and 3 also demonstrated the association between carriers of the G allele and lower BMI ratings (sample 2, P=.05; sample 3, P=.0003).

In a combination analysis, which excluded patients taking additional weight-gain inducing medications, a generalized additive mixed model indicated that the 98 patients with the G allele had a 1.81 kg/m2 lower BMI than those without (n=226; P<.0001). In the sex-stratified analysis, G allele carrying-women aged younger than 45 years had a significant BMI decrease of 3.87 kg/m(P<.0001).

Besides genetic polymorphism, researchers also found age (0.09 kg/m2increase per year; P<.0001) and the type of psychotropic medication (1.41 kg/m2 higher with antipsychotics vs. mood stabilizers; P=.003) impacted BMI.

In the population-based samples, Cohorte Lausannoise (n=5,338; CoLaus) and the Genetic Investigation of Anthropometric Traits (n=123,865; GIANT), researchers analyzed data for the CRTC1 polymorphism rs6510997C>T.

In CoLaus, researchers found no link between the T allele (T allele of rs6510997 being a proxy of the rs3746266 G allele) and BMI, weight and waist circumference. However, there was a significant association with fat mass (P=.03), with the strongest association apparent in premenopausal women (n=1,192; P=.02). The T allele was associated with a lower BMI in the GIANT study, with each copy of the T allele reducing BMI by 0.02 SD unit (P=.01).

“The discovery of other genes influencing obesity in psychiatric populations and future prospective, controlled studies in psychiatric patients with (pharmaco)genetic tests are needed to individualize the clinical care and pharmacological treatment,” Eap said.

Source: Endocrine Today.


Radar gun spots vehicles with illegal GPS jammers.

The battle against truckers and motorists who jam GPS signals has moved up a gear. A new handheld radar can pinpoint which vehicles are illegally using the jammers to stop their bosses from monitoring where they drive or to dodge automatic tolls on motorways.

Used just like a speed gun, a police or customs officer would train the iPad sized gadget, made by Chronos Technology of Lydbrook, UK, on queues of traffic – or even on people walking down the street.

The device is needed because a £50 jammer, which can be bought online, can cause widespread GPS outages thanks to the low power of the GPS satellite signal.


Until now, says navigation engineer Ian Cotts at Chronos, a number of devices have been available to law enforcement officers who want to detect jammers. But existing detectors can only detect the presence of a jammer, not find out where it is.

Chronos says that its £1600 GPS Jammer Detector and Locator System can identify where a jammer-using vehicle is in a multi-storey car park – and can pinpoint portable devices in drivers’ pockets when they have left their cars.

Chronos has not said how the device works, but it is likely it triangulates signal strength to work out exactly where the 1.5 gigahertz signal that a GPS jammer emits is coming from.

Bringing down the system

GPS jamming can cause many problems. For example, a GPS-based landing system at Newark Liberty International Airport malfunctioned twice a day in 2010 – until the source was found to be a driver on the nearby highway using a jammer to avoid paying tolls on outbound and return journeys.

And Chronos, with funding from the UK Technology Strategy Board, last year confirmed with a network of covert receivers that GPS jamming is rife in the UK.

This week, for example, The Economist reported that the area around the London Stock Exchange is suffering daily outages. No one yet knows what is causing it.

That matters because a GPS jammer does not only scupper the operation of satellite navigation systems within about 300 metres. It can also disrupt the reception of the atomic-clock-based timing signals from GPS satellites. These signals are used by financial institutions to time stamp transactions and by utility companies to synchronise power grid operations.

The growing volume of GPS jamming cases has finally led to the UK telecoms regulator, Ofcom, addressing the fact that is illegal to use but legal to own a jammer. “We are consulting with government right now on the potential introduction of regulations to prohibit ownership of GPS jammers,” a spokesman said.


Switching HIV Treatment in Adults Based on CD4 Count Versus Viral Load Monitoring: A Randomized, Non-Inferiority Trial in Thailand.



Viral load (VL) is recommended for monitoring the response to highly active antiretroviral therapy (HAART) but is not routinely available in most low- and middle-income countries. The purpose of the study was to determine whether a CD4-based monitoring and switching strategy would provide a similar clinical outcome compared to the standard VL-based strategy in Thailand.

Methods and Findings

The Programs for HIV Prevention and Treatment (PHPT-3) non-inferiority randomized clinical trial compared a treatment switching strategy based on CD4-only (CD4) monitoring versus viral-load (VL). Consenting participants were antiretroviral-naïve HIV-infected adults (CD4 count 50–250/mm3) initiating non-nucleotide reverse transcriptase inhibitor (NNRTI)-based therapy. Randomization, stratified by site (21 public hospitals), was performed centrally after enrollment. Clinicians were unaware of the VL values of patients randomized to the CD4 arm. Participants switched to second-line combination with confirmed CD4 decline >30% from peak (within 200 cells from baseline) in the CD4 arm, or confirmed VL >400 copies/ml in the VL arm. Primary endpoint was clinical failure at 3 years, defined as death, new AIDS-defining event, or CD4 <50 cells/mm3. The 3-year Kaplan-Meier cumulative risks of clinical failure were compared for non-inferiority with a margin of 7.4%. In the intent to treat analysis, data were censored at the date of death or at last visit. The secondary endpoints were difference in future-drug-option (FDO) score, a measure of resistance profiles, virologic and immunologic responses, and the safety and tolerance of HAART. 716 participants were randomized, 356 to VL monitoring and 360 to CD4 monitoring. At 3 years, 319 participants (90%) in VL and 326 (91%) in CD4 were alive and on follow-up. The cumulative risk of clinical failure was 8.0% (95% CI 5.6–11.4) in VL versus 7.4% (5.1–10.7) in CD4, and the upper-limit of the one-sided 95% CI of the difference was 3.4%, meeting the pre-determined non-inferiority criterion. Probability of switch for study criteria was 5.2% (3.2–8.4) in VL versus 7.5% (5.0–11.1) in CD4 (p = 0.097). Median time from treatment initiation to switch was 11.7 months (7.7–19.4) in VL and 24.7 months (15.9–35.0) in CD4 (p = 0.001). The median duration of viremia >400 copies/ml at switch was 7.2 months (5.8–8.0) in VL versus 15.8 months (8.5–20.4) in CD4 (p = 0.002). FDO scores were not significantly different at time of switch. No adverse events related to the monitoring strategy were reported.


The 3-year rates of clinical failure and loss of treatment options did not differ between strategies although the longer-term consequences of CD4 monitoring would need to be investigated. These results provide reassurance to treatment programs currently based on CD4 monitoring as VL measurement becomes more affordable and feasible in resource-limited settings.

Source: PLOS