Importance of early weight changes to predict long-term weight gain during psychotropic drug treatment.

BACKGROUND: Psychotropic drugs can induce substantial weight gain, particularly during the first 6 months of treatment. The authors aimed to determine the potential predictive power of an early weight gain after the introduction of weight gain-inducing psychotropic drugs on long-term weight gain. METHOD: Data were obtained from a 1-year longitudinal study ongoing since 2007 including 351 psychiatric (ICD-10) patients, with metabolic parameters monitored (baseline and/or 1, 3, 6, 9, 12 months) and with compliance ascertained. International Diabetes Federation and World Health Organization definitions were used to define metabolic syndrome and obesity, respectively. RESULTS: Prevalences of metabolic syndrome and obesity were 22% and 17%, respectively, at baseline and 32% and 24% after 1 year. Receiver operating characteristic analyses indicated that an early weight gain > 5% after a period of 1 month is the best predictor for important long-term weight gain (≥ 15% after 3 months: sensitivity, 67%; specificity, 88%; ≥ 20% after 12 months: sensitivity, 47%; specificity, 89%). This analysis identified most patients (97% for 3 months, 93% for 12 months) who had weight gain ≤ 5% after 1 month as continuing to have a moderate weight gain after 3 and 12 months. Its predictive power was confirmed by fitting a longitudinal multivariate model (difference between groups in 1 year of 6.4% weight increase as compared to baseline, P = .0001). CONCLUSION: Following prescription of weight gain-inducing psychotropic drugs, a 5% threshold for weight gain after 1 month should raise clinician concerns about weight-controlling strategies

Quantification of CH4_{4} emissions from waste disposal sites near the city of Madrid using ground- and space-based observations of COCCON, TROPOMI and IASI

The objective of this study is to derive methane (CH$_{4}$) emissions from three landfills, which are found to be the most significant CH$_{4}$ sources in the metropolitan area of Madrid in Spain. We derive CH$_{4}$ emissions from the CH$_{4}$ enhancements observed by spaceborne and ground-based instruments. We apply satellite-based measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) together with measurements from the ground-based COllaborative Carbon Column Observing Network (COCCON) instruments. In 2018, a 2-week field campaign for measuring the atmospheric concentrations of greenhouse gases was performed in Madrid in the framework of Monitoring of the Greenhouse Gases Concentrations in Madrid (MEGEI-MAD) project. Five COCCON instruments were deployed at different locations around the Madrid city center, enabling the observation of total column-averaged CH$_{4}$ mixing ratios (XCH$_{4}$). Considering the prevalent wind regimes, we calculate the wind-assigned XCH$_{4}$ anomalies for two opposite wind directions. Pronounced bipolar plumes are found when applying the method to NO$_{2}$, which implies that our method of wind-assigned anomaly is suitable to estimate enhancements of trace gases at the urban level from satellite-based measurements. For quantifying the CH$_{4}$ emissions, the wind-assigned plume method is applied to the TROPOMI XCH$_{4}$ and to the lower tropospheric CH$_{4}$  dry-air column ratio (TXCH$_{4}$) of the combined TROPOMI+IASI product. As CH$_{4}$ emission strength we estimate 7.4 × 10$^{25}$ ± 6.4 × 10$^{24}$ molec. s$^{-1}$ from the TROPOMI XCH$_{4}$ data and 7.1 × 10$^{25}$ ± 1.0 × 10$^{25}$ molec. s$^{-1}$ from the TROPOMI+IASI merged TXCH$_{4}$ data. We use COCCON observations to estimate the local source strength as an independent method. COCCON observations indicate a weaker CH$_{4}$ emission strength of 3.7 × 1025 molec. s$^{-1}$ from a local source (the Valdemingómez waste plant) based on observations from a single day. This strength is lower than the one derived from the satellite observations, and it is a plausible result. This is because the analysis of the satellite data refers to a larger area, covering further emission sources in the study region, whereas the signal observed by COCCON is generated by a nearby local source. All emission rates estimated from the different observations are significantly larger than the emission rates provided via the official Spanish Register of Emissions and Pollutant Sources

Early changes of blood lipid levels during psychotropic drug treatment as predictors of long-term lipid changes and of new onset dyslipidemia.

Cardiovascular diseases and dyslipidemia represent a major health issue in psychiatry. Many psychotropic drugs can induce a rapid and substantial increase of blood lipid levels. This study aimed to determine the potential predictive power of an early change of blood lipid levels during psychotropic treatment on long-term change and on dyslipidemia development. Data were obtained from a prospective study including 181 psychiatric patients with metabolic parameters monitored during the first year of treatment and with adherence ascertained. Blood lipid levels (ie, total cholesterol [TC], low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C], non-high-density lipoprotein cholesterol [non-HDL-C], and fasting triglycerides [TGs]) were measured at baseline and after 1, 3, and/or 12 months of treatment. Receiver-operating characteristic analyses indicated that early (ie, after 1 month of psychotropic treatment) increases (≥5%) for TC, LDL-C, TG, and non-HDL-C and decrease (≥5%) for HDL-C were the best predictors for clinically relevant modifications of blood lipid levels after 3 months of treatment (≥30% TC, ≥40% LDL-C, ≥45% TG, ≥55% non-HDL-C increase, and ≥20% HDL-C decrease; sensitivity 70%-100%, specificity 53%-72%). Predictive powers of these models were confirmed by fitting longitudinal multivariate models in the same cohort (P ≤ .03) as well as in a replication cohort (n = 79; P ≤ .003). Survival models showed significantly higher incidences of new onset dyslipidemia (TC, LDL-C, and non-HDL-C hypercholesterolemia, HDL-C hypocholesterolemia, and hypertriglyceridemia) for patients with early changes of blood lipid levels compared to others (P ≤ .01). Early modifications of blood lipid levels following prescription of psychotropic drugs inducing dyslipidemia should therefore raise questions on clinical strategies to control long-term dyslipidemia

Correction: Socio-economic position as a moderator of cardiometabolic outcomes in patients receiving psychotropic treatment associated with weight gain: results from a prospective 12-month inception cohort study and a large population-based cohort.

Weight gain and metabolic complications are major adverse effects of many psychotropic drugs. We aimed to understand how socio-economic status (SES), defined as the Swiss socio-economic position (SSEP), is associated with cardiometabolic parameters after initiation of psychotropic medications known to induce weight gain. Cardiometabolic parameters were collected in two Swiss cohorts following the prescription of psychotropic medications. The SSEP integrated neighborhood-based income, education, occupation, and housing condition. The results were then validated in an independent replication sample (UKBiobank), using educational attainment (EA) as a proxy for SES. Adult patients with a low SSEP had a higher risk of developing metabolic syndrome over one year versus patients with a high SSEP (Hazard ratio (95% CI) = 3.1 (1.5–6.5), n = 366). During the first 6 months of follow-up, a significant negative association between SSEP and body mass index (BMI), weight change, and waist circumference change was observed (25 ≤ age < 65, n = 526), which was particularly important in adults receiving medications with the highest risk of weight gain, with a BMI difference of 0.86 kg/m(2) between patients with low versus high SSEP (95% CI: 0.03–1.70, n = 99). Eventually, a causal effect of EA on BMI was revealed using Mendelian randomization in the UKBiobank, which was notably strong in high-risk medication users (beta: −0.47 SD EA per 1 SD BMI; 95% CI: −0.46 to −0.27, n = 11,314). An additional aspect of personalized medicine was highlighted, suggesting the patients’ SES represents a significant risk factor. Particular attention should be paid to patients with low SES when initiating high cardiometabolic risk psychotropic medications

Ground-Based Mobile Measurements to Track Urban Methane Emissions from Natural Gas in 12 Cities across Eight Countries

International audienceTo mitigate methane emission from urban natural gas distribution systems, it is crucial to understand local leak rates and occurrence rates. To explore urban methane emissions in cities outside the U.S., where significant emissions were found previously, mobile measurements were performed in 12 cities across eight countries. The surveyed cities range from medium size, like Groningen, NL, to large size, like Toronto, CA, and London, UK. Furthermore, this survey spanned across European regions from Barcelona, ES, to Bucharest, RO. The joint analysis of all data allows us to focus on general emission behavior for cities with different infrastructure and environmental conditions. We find that all cities have a spectrum of small, medium, and large methane sources in their domain. The emission rates found follow a heavytailed distribution, and the top 10% of emitters account for 60-80% of total emissions, which implies that strategic repair planning could help reduce emissions quickly. Furthermore, we compare our findings with inventory estimates for urban natural gas-related methane emissions from this sector in Europe. While cities with larger reported emissions were found to generally also have larger observed emissions, we find clear discrepancies between observation-based and inventory-based emission estimates for our 12 cities

Seven years of recent European net terrestrial carbon dioxide exchange constrained by atmospheric observations

We present an estimate of net ecosystem exchange (NEE) of CO2 in Europe for the years 2001-2007. It is derived with a data assimilation that uses a large set of atmospheric CO2 mole fraction observations (~70 000) to guide relatively simple descriptions of terrestrial and oceanic net exchange, while fossil fuel and fire emissions are prescribed. Weekly terrestrial sources and sinks are optimized (i.e., a flux inversion) for a set of 18 large ecosystems across Europe in which prescribed climate, weather, and surface characteristics introduce finer scale gradients. We find that the terrestrial biosphere in Europe absorbed a net average of -165 Tg C yr-1 over the period considered. This uptake is predominantly in non-EU countries, and is found in the northern coniferous (-94 Tg C yr-1) and mixed forests (-30 Tg C yr-1) as well as the forest/field complexes of eastern Europe (-85 Tg C yr-1). An optimistic uncertainty estimate derived using three biosphere models suggests the uptake to be in a range of -122 to -258 Tg C yr-1, while a more conservative estimate derived from the a-posteriori covariance estimates is -165±437 Tg C yr-1. Note, however, that uncertainties are hard to estimate given the nature of the system and are likely to be significantly larger than this. Interannual variability in NEE includes a reduction in uptake due to the 2003 drought followed by 3 years of more than average uptake. The largest anomaly of NEE occurred in 2005 concurrent with increased seasonal cycles of observed CO2. We speculate these changes to result from the strong negative phase of the North Atlantic Oscillation in 2005 that lead to favorable summer growth conditions, and altered horizontal and vertical mixing in the atmosphere. All our results are available through http://www.carbontracker.eu

Ground-Based Mobile Measurements to Track Urban Methane Emissions from Natural Gas in 12 Cities across Eight Countries

To mitigate methane emission from urban natural gas distribution systems, it is crucial to understand local leak rates and occurrence rates. To explore urban methane emissions in cities outside the U.S., where significant emissions were found previously, mobile measurements were performed in 12 cities across eight countries. The surveyed cities range from medium size, like Groningen, NL, to large size, like Toronto, CA, and London, UK. Furthermore, this survey spanned across European regions from Barcelona, ES, to Bucharest, RO. The joint analysis of all data allows us to focus on general emission behavior for cities with different infrastructure and environmental conditions. We find that all cities have a spectrum of small, medium, and large methane sources in their domain. The emission rates found follow a heavy-tailed distribution, and the top 10% of emitters account for 60–80% of total emissions, which implies that strategic repair planning could help reduce emissions quickly. Furthermore, we compare our findings with inventory estimates for urban natural gas-related methane emissions from this sector in Europe. While cities with larger reported emissions were found to generally also have larger observed emissions, we find clear discrepancies between observation-based and inventory-based emission estimates for our 12 cities