Evolución de la prevalencia de sedentarismo en la población española entre los años 1987 y 2020 / Evolution of sedentarism prevalence in Spanish population between 1987 and 2020

Antecedentes y objetivo: El sedentarismo es un factor predictivo para numerosas enfermedades. El objetivo de este estudio fue valorar la evolución de la prevalencia de sedentarismo en la población española adulta entre los años 1987-2020. Métodos: Las fuentes de datos fueron las Encuestas Nacionales y Europeas de Salud. La prevalencia de sedentarismo se valoró en 3 escenarios (actividad principal, tiempo libre y todos los escenarios). Se estimaron prevalencias de sedentarismo global, por sexo y grupo de edad. En todos los escenarios la prevalencia también se estimó por comunidad autónoma. La tendencia de las prevalencias se analizó con los porcentajes de cambio anual (PCA) obtenidos a través de modelos joinpoint. Resultados: La prevalencia de sedentarismo en la actividad principal varió entre el 31,2% en 1987 y el 38,4% en 2020 (PCA: 0,7 [0,5 a 1,0]), siendo mayor en los varones que en las mujeres, y más elevada en los más jóvenes y en los más mayores. La prevalencia de sedentarismo en el tiempo libre varió entre el 55,1% en 1993 y el 36,4% en 2020 (PCA: −1,4 [−1,9 a −0,9]), siendo siempre superior en las mujeres, más alta en los mayores de 64 años y menor en los de 16-24 años. Cantabria y Canarias fueron las comunidades autónomas con la prevalencia de sedentarismo más baja en todos los escenarios. Conclusiones: La prevalencia de sedentarismo en la actividad principal está aumentando en España, mientras que durante el tiempo libre está descendiendo. Es importante aplicar medidas de prevención y promoción de la salud dirigidas a disminuir el sedentarismo en la población.(AU)

Background and objective: Sedentary behavior is a predictive factor for numerous diseases. The objective of this study was to assess the evolution of the prevalence of sedentary behavior in the Spanish adult population between 1987 and 2020. Methods: The data sources were the National and European Health Surveys. The prevalence of sedentary behavior was assessed in three scenarios (main activity, leisure time and all scenarios). Prevalence of sedentary behavior was estimated overall, by sex and age group. In all scenarios, prevalence was also estimated by Autonomous Community. The prevalence trend was analyzed with the annual percent change (APC) obtained through joinpoint models. Results: The prevalence of sedentary in the main activity ranged from 31.2% in 1987 to 38.4% in 2020 [PCA: 0.7 (0.5-1.0)], being higher in men than in women and higher in younger and older people. The prevalence of sedentary in the leisure time varied between 55.1% in 1993 and 36.4% in 2020 [PCA: −1.4 (−1.9 to −0.9)], being always higher in women, higher in those over 64 years of age and lower in those aged 16–24 years. Cantabria and the Canary Islands were the Autonomous Communities with the lowest prevalence of sedentary behavior in all scenarios. Conclusions: The prevalence of sedentary behavior in the main activity is increasing in Spain, whereas during leisure time it is decreasing. It is important to implement prevention and health promotion measures aimed at reducing sedentary behavior in the population.(AU)

Evolution of sedentarism prevalence in Spanish population between 1987 and 2020. / Evolución de la prevalencia de sedentarismo en la población española entre los años 1987 y 2020.

BACKGROUND AND OBJECTIVE: Sedentary behavior is a predictive factor for numerous diseases. The objective of this study was to assess the evolution of the prevalence of sedentary behavior in the Spanish adult population between 1987 and 2020. METHODS: The data sources were the National and European Health Surveys. The prevalence of sedentary behavior was assessed in three scenarios (main activity, leisure time and all scenarios). Prevalence of sedentary behavior was estimated overall, by sex and age group. In all scenarios, prevalence was also estimated by Autonomous Community. The prevalence trend was analyzed with the annual percent change (APC) obtained through joinpoint models. RESULTS: The prevalence of sedentary in the main activity ranged from 31.2% in 1987 to 38.4% in 2020 [PCA: 0.7 (0.5-1.0)], being higher in men than in women and higher in younger and older people. The prevalence of sedentary in the leisure time varied between 55.1% in 1993 and 36.4% in 2020 [PCA: -1.4 (-1.9 to -0.9)], being always higher in women, higher in those over 64 years of age and lower in those aged 16-24 years. Cantabria and the Canary Islands were the Autonomous Communities with the lowest prevalence of sedentary behavior in all scenarios. CONCLUSIONS: The prevalence of sedentary behavior in the main activity is increasing in Spain, whereas during leisure time it is decreasing. It is important to implement prevention and health promotion measures aimed at reducing sedentary behavior in the population.

Proof of concept of the potential of a machine learning algorithm to extract new information from conventional SARS-CoV-2 rRT-PCR results.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been and remains one of the major challenges modern society has faced thus far. Over the past few months, large amounts of information have been collected that are only now beginning to be assimilated. In the present work, the existence of residual information in the massive numbers of rRT-PCRs that tested positive out of the almost half a million tests that were performed during the pandemic is investigated. This residual information is believed to be highly related to a pattern in the number of cycles that are necessary to detect positive samples as such. Thus, a database of more than 20,000 positive samples was collected, and two supervised classification algorithms (a support vector machine and a neural network) were trained to temporally locate each sample based solely and exclusively on the number of cycles determined in the rRT-PCR of each individual. Overall, this study suggests that there is valuable residual information in the rRT-PCR positive samples that can be used to identify patterns in the development of the SARS-CoV-2 pandemic. The successful application of supervised classification algorithms to detect these patterns demonstrates the potential of machine learning techniques to aid in understanding the spread of the virus and its variants.

Case report: BA.1 subvariant showing a BA.2-like pattern using a variant-specific PCR assay due to a single point mutation downstream the spike 69/70 deletion.

BACKGROUND: SARS-CoV-2 variant tracking is key to the genomic surveillance of the COVID-19 pandemic. While next-generation sequencing (NGS) is commonly used for variant determination, it is expensive and time-consuming. Variant-specific PCR (vsPCR) is a faster, cheaper method that detects specific mutations that are considered variant-defining. These tests usually rely on specific amplification when a mutation is present or a specific melting temperature peak after amplification. CASE PRESENTATION: A discrepant result between vsPCR and NGS was found in seventeen SARS-CoV-2 samples from Galicia, Spain. A cluster of BA.1 Omicron SARS-CoV-2 variant showed a BA.2-like melting temperature pattern due to a point mutation (C21772T) downstream the deletion of the spike amino acids 69/70. As the 69/70 deletion is widely used for differentiation between BA.1 and BA.2 by vsPCR, C21772T can cause BA.1 samples to be misinterpreted as BA.2. Over a thousand BA.1 sequences in the EpiCoV database contain this mutation. CONCLUSIONS: To our knowledge, this is the first case of a point mutation causing a vsPCR algorithm to misclassify BA.1 samples as BA.2. This is an example of how mutations in the probe target area of vsPCR tests based on melting curve analysis can lead to variant misclassification. NGS confirmation of vsPCR results is relevant for the accuracy of the epidemiological surveillance. In order to overcome the possible impact of novel mutations, diagnostic tools must be constantly updated.

Capillary zone electrophoresis method for the determination of inorganic anions and formic acid in honey.

A capillary zone electrophoresis method for the determination of inorganic anions and formic acid in honey samples was developed for the first time. The complete separation of chloride, nitrate, sulfate, phosphate, and formic acid was achieved with a simple electrolyte composed by 2 mM potassium dichromate as the carrier solution and background absorbance provider and 0.05 mM tetraethylenepentamine (TEPA) as electro-osmotic flow suppressor (pH 4.00). Injection was performed hydrostatically by elevating the sample at 10 cm for 10 s. The running voltage was -27 kV at 25 degrees C. Indirect UV absorption detection was achieved at 254 nm. The detection limit was in the range between 0.03 and 20 mg/kg, and the quantification limits ranged from 1.52 to 20.6 mg/kg. The calibration graphs were linear in the concentration range from the quantification limit to at least 2.5 g/kg for chloride, 0.25 g/kg for nitrate, 0.75 g/kg for sulfate, 1.50 g/kg for phosphate, and 0.75 g/kg for formic acid. Precision data in the honey samples analyzed showed repeatability and reproducibility relative standard deviations lower than 1.4 and 2.4% for migration time and lower than 1.8 and 4.3% for anion content, respectively. Recoveries of anions in honey samples analyzed ranged from 94.4 to 99.8%. Ten honey samples were analyzed to test the proposed method. Mean contents of 260.5, 3.93, 60.5, 139.4, and 209.3 mg/kg were found, respectively, for chloride, nitrate, sulfate, phosphate, and formic acid in analyzed honeys. These results agreed with literature data.

Rapid capillary zone electrophoresis method for the determination of metal cations in beverages.

A rapid and reliable capillary zone electrophoresis method for the determination of inorganic cations was developed. The complete separation of K(+), Ba(2+), Ca(2+), Na(+), Mg(2+), Mn(2+), Ni(2+), Cd(2+), Li(+) and Cu(2+) can be achieved in 4min with a simple electrolyte composed by 10mM imidazole as the carrier buffer and background absorbance provider and acetic acid as the complexing agent (pH 3.60). Injection was performed hydrostatically by elevating the sample at 10cm for 30s. The running voltage was +25kV at room temperature. Indirect UV-absorption detection was achieved at 185nm. The detection limit was in the range between 0.06mg/l (Mg(2+)) and 0.57mg/l (K(+)) and the quantification limits ranged from 0.10mg/l (Ni(2+)) to 0.80mg/l (Cu(2+)). The calibration graphs were linear in the concentration range from the quantification limit till at least 1g/l in K(+), 10mg/l in Ba(2+), Ca(2+), Mg(2+), Mn(2+), Ni(2+) and Cd(2+), 40mg/l in Na(+) and 12mg/l in Li(+) and Cu(2+). The repeatability, intraday and interday analysis were

Capillary zone electrophoresis method for the simultaneous determination of cations in honey.

A capillary electrophoresis system for the simultaneous determination of cations in honey samples has been developed. The complete separation and quantification of K+, Ca2+, Na+, Mg2+, Mn2+, Ni2+ and Li+, which represent more than 99% of the total content of cations in honey, can be achieved in 4 min with only a dilution and filtration of the honey sample. Electrolyte solution was composed by 10 mM imidazole as the carrier buffer and background absorbance provider and acetic acid as the complexing agent (pH 3.60). The running voltage was + 25 kV at 25 degree C. Indirect UV detection was achieved at 185 nm. Under the optimum conditions the detection limits ranged from 0.02 to 48.2 mg/kg and the quantification limits have ranged from 0.41 to 48.7 mg/kg. Precision data in honey samples analysed have shown repeatability and reproducibility RSD (%) lower than 2.84 and 6.62%, respectively. Recoveries of cations in honey samples analysed have ranged from 88.5 to 101.8%. These cations were identified by their relative migration times with regard to Ba2+ migration time used as reference standard and they were quantified by using an external standard calibration. Twenty-five honey samples were analysed to test the proposed method. Mean contents of 1.22 x 10(3), 93, 85, 54, 11, 1.9 and 2.3 mg/kg were found, respectively, for K+, Ca2+, Na+, Mg2+, Mn2+, Ni2+ and Li+ cations in analysed honeys. These results were similar than the obtained by other authors.

Rapid determination of minority organic acids in honey by high-performance liquid chromatography.

A rapid high-performance liquid chromatographic method for the determination of organic acids in honey is reported. Malic, maleic, citric, succinic and fumaric acids were identified and quantified in 15 min. First time repeatibility, reproducibility and recoveries were determined out for these acids in honey samples. Maleic acid was also quantified for first time by a chromatographic method. The organic acids were removed from honey by using a solid-phase extraction procedure with anion-exchange cartridges. Previously, the solution of honey was adjusted to pH 10.50 with 0.1 M NaOH and stirred for 15 min at room temperature. Then, this solution was adjusted to pH 5.00 with 0.1 M H2SO4. This procedure was carried out to avoid interferences in the baseline. The chromatographic separation was achieved with only one Spherisorb ODS-2 S5 column thermostated at 25 degrees C. Metaphosphoric acid (pH 2.20) was used as mobile phase at a flow-rate of 0.7 ml/min. Organic acids were detected with a UV-vis detector (215 nm). The precision results showed that the relative standard deviations of the repeatability and reproducibility were < or =3.20% and < or =4.86%, respectively. The recoveries of the organic acids ranged from 62.9 to 99.4%. Under optimum conditions the detection limits ranged from 0.0064 to 7.57 mg/kg and the quantification limits ranged from 0.025 to 10.93 mg/kg.

Solid-phase extraction procedure to remove organic acids from honey.

A solid-phase extraction procedure was applied to remove organic acids from honey. Malic, maleic, citric, succinic and fumaric acids were isolated with an anion-exchange cartridge. The different parameters which affected the extraction procedure were studied and optimised to establish the optimal conditions for maximum recovery of organic acids and minimum extraction of interferences. The optimised procedure used a cartridge which was activated with 10 ml of 0.1 M sodium hydroxide solution (percolation rate 3 ml/min). A 10 ml volume of honey solution was passed at a flow-rate of 0.5 ml/min. The cartridge was washed with 10 ml of water (3 ml/min) and organic acids were eluted with 4 ml of 0.1 M sulfuric acid (0.5 ml/min). This solution was injected directly into the chromatograph. When this procedure was carried out on standard solutions of organic acids, recoveries between 99.2 and 103.4% were found. If this procedure was applied to honey samples these recoveries were also satisfactory and ranged from 62.9 to 99.4%.