Heart rate variability in hypothyroid patients:a systematic review and meta-analysis

INTRODUCTION: Hypothyroidism may be associated with changes in the autonomic regulation of the cardiovascular system, which may have clinical implications. OBJECTIVE: To conduct a systematic review and meta-analysis on the impact of hypothyroidism on HRV. MATERIALS AND METHODS: PubMed, Cochrane, Embase and Google Scholar were searched until 20 August 2021 for articles reporting HRV parameters in untreated hypothyroidism and healthy controls. Random-effects meta-analysis were stratified by degree of hypothyroidism for each HRV parameters: RR intervals (or normal to normal-NN intervals), SDNN (standard deviation of RR intervals), RMSSD (square root of the mean difference of successive RR intervals), pNN50 (percentage of RR intervals with >50ms variation), total power (TP), LFnu (low-frequency normalized unit), HFnu (high-frequency), VLF (very low frequency), and LF/HF ratio. RESULTS: We included 17 studies with 11438 patients: 1163 hypothyroid patients and 10275 healthy controls. There was a decrease in SDNN (effect size = -1.27, 95% CI -1.72 to -0.83), RMSSD (-1.66, -2.32 to -1.00), pNN50 (-1.41, -1.98 to -0.84), TP (-1.55, -2.1 to -1.00), HFnu (-1.21, -1.78 to -0.63) with an increase in LFnu (1.14, 0.63 to 1.66) and LF/HF ratio (1.26, 0.71 to 1.81) (p <0.001). HRV alteration increased with severity of hypothyroidism. CONCLUSIONS: Hypothyroidism is associated with a decreased HRV, that may be explained by molecular mechanisms involving catecholamines and by the effect of TSH on HRV. The increased sympathetic and decreased parasympathetic activity may have clinical implications

Stochastic upscaling of hydrodynamic dispersion and retardation factor in a physically and chemically heterogeneous tropical soil

[EN] Stochastic upscaling of flow and reactive solute transport in a tropical soil is performed using real data collected in the laboratory. Upscaling of hydraulic conductivity, longitudinal hydrodynamic dispersion, and retardation factor were done using three different approaches of varying complexity. How uncertainty propagates after upscaling was also studied. The results show that upscaling must be taken into account if a good reproduction of the flow and transport behavior of a given soil is to be attained when modeled at larger than laboratory scales. The results also show that arrival time uncertainty was well reproduced after solute transport upscaling. This work represents a first demonstration of flow and reactive transport upscaling in a soil based on laboratory data. It also shows how simple upscaling methods can be incorporated into daily modeling practice using commercial flow and transport codes.The authors thank the financial support by the Brazilian National Council for Scientific and Technological Development (CNPq) (Project 401441/2014-8). The doctoral fellowship award to the first author by the Coordination of Improvement of Higher Level Personnel (CAPES) is acknowledged. The first author also thanks the international mobility grant awarded by CNPq, through the Sciences Without Borders program (Grant Number: 200597/2015-9). The international mobility grant awarded by Santander Mobility in cooperation with the University of Sao Paulo is also acknowledged. DHI-WASI is gratefully thanked for providing a FEFLOW license.Almeida De-Godoy, V.; Zuquette, L.; Gómez-Hernández, JJ. (2019). Stochastic upscaling of hydrodynamic dispersion and retardation factor in a physically and chemically heterogeneous tropical soil. Stochastic Environmental Research and Risk Assessment. 33(1):201-216. https://doi.org/10.1007/s00477-018-1624-zS201216331Ahuja LR, Naney JW, Green RE, Nielsen DR (1984) Macroporosity to characterize spatial variability of hydraulic conductivity and effects of land management. Soil Sci Soc Am J 48:699. https://doi.org/10.2136/sssaj1984.03615995004800040001xBellin A, Lawrence AE, Rubin Y (2004) Models of sub-grid variability in numerical simulations of solute transport in heterogeneous porous formations: three-dimensional flow and effect of pore-scale dispersion. Stoch Environ Res Risk Assess 18:31–38. https://doi.org/10.1007/s00477-003-0164-2Brent RP (1973) Algorithms for minimization without derivatives. Prentice Hall, Englewood CliffsBrusseau ML (1998) Non-ideal transport of reactive solutes in heterogeneous porous media: 3. model testing and data analysis using calibration versus prediction. J Hydrol 209:147–165. https://doi.org/10.1016/S0022-1694(98)00121-8Brusseau ML, Srivastava R (1999) Nonideal transport of reactive solutes in heterogeneous porous media: 4. Analysis of the cape cod natural-gradient field experiment. Water Resour Res 35:1113–1125. https://doi.org/10.1029/1998WR900019Brutsaert W (1967) Some methods of calculating unsaturated permeability. Trans ASAE 10:400–404Cadini F, De Sanctis J, Bertoli I, Zio E (2013) Upscaling of a dual-permeability Monte Carlo simulation model for contaminant transport in fractured networks by genetic algorithm parameter identification. Stoch Environ Res Risk Assess 27:505–516. https://doi.org/10.1007/s00477-012-0595-8Cambardella CA, Moorman TB, Parkin TB, Karlen DL, Novak JM, Turco RF, Konopka AE (1994) Field-scale variability of soil properties in central iowa soils. Soil Sci Soc Am J 58:1501. https://doi.org/10.2136/sssaj1994.03615995005800050033xCapilla JE, Rodrigo J, Gómez-Hernández JJ (1999) Simulation of non-Gaussian transmissivity fields honoring piezometric data and integrating soft and secondary information. Math Geol 31:907–927. https://doi.org/10.1023/A:1007580902175Cassiraga EF, Fernàndez-Garcia D, Gómez-Hernández JJ (2005) Performance assessment of solute transport upscaling methods in the context of nuclear waste disposal. Int J Rock Mech Min Sci 42:756–764. https://doi.org/10.1016/j.ijrmms.2005.03.013Corey AT (1977) Mechanics of heterogeneous fluids in porous media. Water Resources Publications, Fort Collins, CO, p 259Dagan G (1989) Flow and transport in porous formations. Springer, Berlin. https://doi.org/10.1007/978-3-642-75015-1Dagan G (2004) On application of stochastic modeling of groundwater flow and transport. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-004-0191-7de Azevedo AAB, Pressinotti MMN, Massoli M (1981) Sedimentological studies of the Botucatu and Pirambóia formations in the region of Santa Rita do Passa Quatro (In portuguese). Rev do Inst Geológico 2:31–38. https://doi.org/10.5935/0100-929X.19810003Deng H, Dai Z, Wolfsberg AV, Ye M, Stauffer PH, Lu Z, Kwicklis E (2013) Upscaling retardation factor in hierarchical porous media with multimodal reactive mineral facies. Chemosphere 91:248–257. https://doi.org/10.1016/j.chemosphere.2012.10.105Diersch H-JG (2014) Finite element modeling of flow, mass and heat transport in porous and fractured media. Springer, Berlin. https://doi.org/10.1007/978-3-642-38739-5Dippenaar MA (2014) Porosity reviewed: quantitative multi-disciplinary understanding, recent advances and applications in vadose zone hydrology. Geotech Geol Eng 32:1–19. https://doi.org/10.1007/s10706-013-9704-9Fagundes JRT, Zuquette LV (2011) Sorption behavior of the sandy residual unconsolidated materials from the sandstones of the Botucatu Formation, the main aquifer of Brazil. Environ Earth Sci 62:831–845. https://doi.org/10.1007/s12665-010-0570-yFenton GA, Griffiths DV (2008) Risk assessment in geotechnical engineering. Wiley, p 463Fernàndez-Garcia D, Gómez-Hernández JJ (2007) Impact of upscaling on solute transport: Traveltimes, scale dependence of dispersivity, and propagation of uncertainty. Water Resour Res. https://doi.org/10.1029/2005WR004727Fernàndez-Garcia D, Llerar-Meza G, Gómez-Hernández JJ (2009) Upscaling transport with mass transfer models: mean behavior and propagation of uncertainty. Water Resour Res. https://doi.org/10.1029/2009WR007764Feyen L, Gómez-Hernández JJ, Ribeiro PJ, Beven KJ, De Smedt F (2003a) A Bayesian approach to stochastic capture zone delineation incorporating tracer arrival times, conductivity measurements, and hydraulic head observations. Water Resour Res. https://doi.org/10.1029/2002WR001544Feyen L, Ribeiro PJ, Gómez-Hernández JJ, Beven KJ, De Smedt F (2003b) Bayesian methodology for stochastic capture zone delineation incorporating transmissivity measurements and hydraulic head observations. J Hydrol 271:156–170. https://doi.org/10.1016/S0022-1694(02)00314-1Forsythe GE, Malcolm MA, Moler CB (1976) Computer methods for mathematical computations. Prentice-Hall, Englewood Cliffs, p 259Freeze R, Cherry J (1979) Groundwater. PrenticeHall Inc, Englewood cliffs, p 604Frippiat CC, Holeyman AE (2008) A comparative review of upscaling methods for solute transport in heterogeneous porous media. J Hydrol 362:150–176. https://doi.org/10.1016/j.jhydrol.2008.08.015Fu J, Gómez-Hernández JJ (2009) Uncertainty assessment and data worth in groundwater flow and mass transport modeling using a blocking Markov chain Monte Carlo method. J Hydrol 364:328–341. https://doi.org/10.1016/j.jhydrol.2008.11.014Gelhar LW, Axness CL (1983) Three-dimensional stochastic analysis of macrodispersion in aquifers. Water Resour Res 19:161–180. https://doi.org/10.1029/WR019i001p00161Gelhar LW, Welty C, Rehfeldt KR (1992) A critical review of data on field-scale dispersion in aquifers. Water Resour Res 28:1955–1974. https://doi.org/10.1029/92WR00607Giacheti HL, Rohm SA, Nogueira JB, Cintra JCA (1993) Geotechnical properties of the Cenozoic sediment (in protuguese). In: Albiero JH, Cintra JCA (eds) Soil from the interior of São Paulo. ABMS, Sao Paulo, pp 143–175Gómez-Hernandez JJ (1990) A stochastic approach to the simulation of block conductivity fields conditional upon data measured at a smaller scale. Stanford University, StanfordGómez-Hernández JJ, Gorelick SM (1989) Effective groundwater model parameter values: influence of spatial variabiity of hydraulic conductivity, leackance, and recharge. Water Resour Res 25:405–419Gómez-Hernández JJ, Journel A (1993) Joint sequential simulation of multigaussian fields. In: Geostatistics Tróia’92. pp 85–94. https://doi.org/10.1007/978-94-011-1739-5_8Gómez-Hernández JJ, Wen X-H (1994) Probabilistic assessment of travel times in groundwater modeling. Stoch Hydrol Hydraul 8:19–55. https://doi.org/10.1007/BF01581389Gómez-Hernández JJ, Fu J, Fernandez-Garcia D (2006) Upscaling retardation factors in 2-D porous media. In: Bierkens MFP, Gehrels JC, Kovar K (eds) Calibration and reliability in groundwater modelling: from uncertainty to decision making: proceedings of the ModelCARE 2005 conference held in The Hague, The Netherlands, 6–9 June, 2005. IAHS Publication, pp 130–136Goovaerts P (1999) Geostatistics in soil science: state-of-the-art and perspectives. Geoderma 89:1–45. https://doi.org/10.1016/S0016-7061(98)00078-0Jarvis NJ (2007) A review of non-equilibrium water fl ow and solute transport in soil macropores: principles, controlling factors and consequences for water quality. Eur J Soil Sci 58:523–546. https://doi.org/10.4141/cjss2011-050Jellali S, Diamantopoulos E, Kallali H, Bennaceur S, Anane M, Jedidi N (2010) Dynamic sorption of ammonium by sandy soil in fixed bed columns: evaluation of equilibrium and non-equilibrium transport processes. J Environ Manag 91:897–905. https://doi.org/10.1016/j.jenvman.2009.11.006Journel AG, Gomez-Hernandez JJ (1993) Stochastic imaging of the wilmington clastic sequence. SPE Form Eval 8:33–40. https://doi.org/10.2118/19857-PAJournel A, Deutsch C, Desbarats A (1986) Power averaging for block effective permeability. Proc SPE Calif Reg Meet. https://doi.org/10.2118/15128-MSKronberg BI, Fyfe WS, Leonardos OH, Santos AM (1979) The chemistry of some Brazilian soils: element mobility during intense weathering. Chem Geol 24:211–229. https://doi.org/10.1016/0009-2541(79)90124-4Lake LW (1988) The origins of anisotropy (includes associated papers 18394 and 18458). J Pet Technol 40:395–396. https://doi.org/10.2118/17652-PALawrence AE, Rubin Y (2007) Block-effective macrodispersion for numerical simulations of sorbing solute transport in heterogeneous porous formations. Adv Water Resour 30:1272–1285. https://doi.org/10.1016/j.advwatres.2006.11.005Lemke LD, Barrack WA II, Abriola LM, Goovaerts P (2004) Matching solute breakthrough with deterministic and stochastic aquifer models. Groundwater 42:920–934Li L, Zhou H, Gómez-Hernández JJ (2011a) A comparative study of three-dimensional hydraulic conductivity upscaling at the macro-dispersion experiment (MADE) site, Columbus Air Force Base, Mississippi (USA). J Hydrol 404:278–293. https://doi.org/10.1016/j.jhydrol.2011.05.001Li L, Zhou H, Gómez-Hernández JJ (2011b) Transport upscaling using multi-rate mass transfer in three-dimensional highly heterogeneous porous media. Adv Water Resour 34:478–489. https://doi.org/10.1016/j.advwatres.2011.01.001Logsdon Keller KE, Moorman TB (2002) Measured and predicted solute leaching from multiple undisturbed soil columns. Soil Sci Soc Am J 66:686–695. https://doi.org/10.2136/sssaj2002.6860Lourens A, van Geer FC (2016) Uncertainty propagation of arbitrary probability density functions applied to upscaling of transmissivities. Stoch Environ Res Risk Assess 30:237–249. https://doi.org/10.1007/s00477-015-1075-8Mahapatra IC, Singh KN, Pillai KG, Bapat SR (1985) Rice soils and their management. Indian J Agron 30:R1–R41Morakinyo JA, Mackay R (2006) Geostatistical modelling of ground conditions to support the assessment of site contamination. Stoch Environ Res Risk Assess 20:106–118. https://doi.org/10.1007/s00477-005-0015-4Moslehi M, de Barros FPJ, Ebrahimi F, Sahimi M (2016) Upscaling of solute transport in disordered porous media by wavelet transformations. Adv Water Resour 96:180–189. https://doi.org/10.1016/j.advwatres.2016.07.013Osinubi KJ, Nwaiwu CM (2005) Hydraulic conductivity of compacted lateritic soil. J Geotech Geoenviron Eng 131:1034–1041. https://doi.org/10.1061/(ASCE)1090-0241(2005)131:8(1034)Remy N (2004) SGeMS: stanford geostatistical modeling software. Softw Man. https://doi.org/10.1007/978-1-4020-3610-1_89Renard P, de Marsily G (1997) Calculating equivalent permeability: a review. Adv Water Resour 20:253–278. https://doi.org/10.1016/S0309-1708(96)00050-4Robin MJL, Sudicky EA, Gillham RW, Kachanoski RG (1991) Spatial variability of strontium distribution coefficients and their correlation with hydraulic conductivity in the Canadian forces base borden aquifer. Water Resour Res 27:2619–2632. https://doi.org/10.1029/91WR01107Salamon P, Fernàndez-Garcia D, Gómez-Hernández JJ (2007) Modeling tracer transport at the MADE site: the importance of heterogeneity. Water Resour Res. https://doi.org/10.1029/2006WR005522Sánchez-Vila X, Carrera J, Girardi JP (1996) Scale effects in transmissivity. J Hydrol 183:1–22. https://doi.org/10.1016/S0022-1694(96)80031-XScheibe T, Yabusaki S (1998) Scaling of flow and transport behavior in heterogeneous groundwater systems. Adv Water Resour 22:223–238. https://doi.org/10.1016/S0309-1708(98)00014-1Selvadurai PA, Selvadurai APS (2014) On the effective permeability of a heterogeneous porous medium: the role of the geometric mean. Philos Mag 94:2318–2338. https://doi.org/10.1080/14786435.2014.913111Shackelford CD (1994) Critical concepts for column testing. J Geotech Eng 120:1804–1828. https://doi.org/10.1016/0148-9062(95)96996-OŠimůnek J, van Genuchten MT, Šejna M, Toride N, Leij FJ (1999) The STANMOD computer software for evaluating solute transport in porous media using analytical solutions of convection-dispersion equation. Riverside, CaliforniaTaskinen A, Sirviö H, Bruen M (2008) Modelling effects of spatial variability of saturated hydraulic conductivity on autocorrelated overland flow data: linear mixed model approach. Stoch Environ Res Risk Assess 22:67–82. https://doi.org/10.1007/s00477-006-0099-5Tuli A, Hopmans JW, Rolston DE, Moldrup P (2005) Comparison of air and water permeability between disturbed and undisturbed soils. Soil Sci Soc Am J 69:1361. https://doi.org/10.2136/sssaj2004.0332Tyukhova AR, Willmann M (2016) Conservative transport upscaling based on information of connectivity. Water Resour Res 52:6867–6880. https://doi.org/10.1002/2015WR018331van Genuchten MTh (1980) Determining transport parameters from solute displacement experiments. Research Report 118. U.S. Salinity Lab., Riverside, CAVanderborght J, Timmerman A, Feyen J (2000) Solute transport for steady-state and transient flow in soils with and without macropores. Soil Sci Soc Am J 64:1305–1317. https://doi.org/10.2136/sssaj2000.6441305xVanmarcke E (2010) Random fields: analysis and synthesis. World Scientific. MIT Press, Cambridge, MA, p 364Vishal V, Leung JY (2017) Statistical scale-up of 3D particle-tracking simulation for non-Fickian dispersive solute transport modeling. Environ Res Risk Assess, Stoch. https://doi.org/10.1007/s00477-017-1501-1Wen X-H, Gómez-Hernández JJ (1996) Upscaling hydraulic conductivities in heterogeneous media: an overview. J Hydrol 183:ix–xxxii. https://doi.org/10.1016/S0022-1694(96)80030-8Wen XH, Gómez-Hernández JJ (1998) Numerical modeling of macrodispersion in heterogeneous media: a comparison of multi-Gaussian and non-multi-Gaussian models. J Contam Hydrol 30:129–156. https://doi.org/10.1016/S0169-7722(97)00035-1Wen XH, Capilla JE, Deutsch CV, Gómez-Hernández JJ, Cullick AS (1999) A program to create permeability fields that honor single-phase flow rate and pressure data. Comput Geosci 25:217–230. https://doi.org/10.1016/S0098-3004(98)00126-5Wilding LP, Drees LR (1983) Spatial variability and pedology. In: Wilding LP, Smeck NE, Hall GF (eds) Pedogenesis and soil taxonomy: the soil orders. Elsevier, Amsterdam, pp 83–116Willmann M, Carrera J, Guadagnini A (2006) Block-upscaling of transport in heterogeneous aquifers. h2ogeo.upc.edu 1–7Xu Z, Meakin P (2013) Upscaling of solute transport in heterogeneous media with non-uniform flow and dispersion fields. Appl Math Model 37:8533–8542. https://doi.org/10.1016/j.apm.2013.03.070Zech A, Attinger S, Cvetkovic V, Dagan G, Dietrich P, Fiori A, Rubin Y, Teutsch G (2015) Is unique scaling of aquifer macrodispersivity supported by field data? Water Resour Res 51:7662–7679. https://doi.org/10.1002/2015WR017220Zhou H, Li L, Gómez-Hernández JJ (2010) Three-dimensional hydraulic conductivity upscaling in groundwater modeling. Comput Geosci 36:1224–1235. https://doi.org/10.1016/j.cageo.2010.03.008Zhou H, Li L, Hendricks Franssen H-J, Gómez-Hernández JJ (2012) Pattern recognition in a bimodal aquifer using the normal-score ensemble Kalman filter. Math Geosci 44:169–185. https://doi.org/10.1007/s11004-011-9372-

Heart Rate Variability in Hyperthyroidism: A Systematic Review and Meta-Analysis

Objective: Cardiovascular effects of thyroid hormones may be measured through heart rate variability (HRV). We sought to determine the impact of hyperthyroidism on HRV. Design: A systematic review and meta-analysis on the impact of hyperthyroidism on HRV. Methods: PubMed, Cochrane, Embase and Google Scholar were searched until 20 August 2021 for articles reporting HRV parameters in untreated hyperthyroidism and healthy controls. Random-effects meta-analysis was stratified by degree of hyperthyroidism for each HRV parameter: RR intervals (or Normal-to-Normal intervals&mdash;NN), SDNN (standard deviation of RR intervals), RMSSD (square root of the mean difference of successive RR intervals), pNN50 (percentage of RR intervals with &gt;50 ms of variation), total power (TP), LFnu (low-frequency normalized unit) and HFnu (high-frequency), VLF (very low-frequency), and LF/HF ratio. Results: We included 22 studies with 10,811 patients: 1002 with hyperthyroidism and 9809 healthy controls. There was a decrease in RR (effect size = &minus;4.63, 95% CI &minus;5.7 to &minus;3.56), SDNN (&minus;6.07, &minus;7.42 to &minus;4.71), RMSSD (&minus;1.52, &minus;2.18 to &minus;0.87), pNN50 (&minus;1.36, &minus;1.83 to &minus;0.88), TP (&minus;2.05, &minus;2.87 to &minus;1.24), HFnu (&minus;3.51, &minus;4.76 to &minus;2.26), and VLF power (&minus;2.65, &minus;3.74 to &minus;1.55), and an increase in LFnu (2.66, 1.55 to 3.78) and LF/HF ratio (1.75, 1.02 to 2.48) (p &lt; 0.01). Most parameters had ES that was twice as high in overt compared to subclinical hyperthyroidism. Increased peripheral thyroid hormones and decreased TSH levels were associated with lower RR intervals. Conclusions: Hyperthyroidism is associated with a decreased HRV, which may be explained by the deleterious effect of thyroid hormones and TSH. The increased sympathetic and decreased parasympathetic activity may have clinical implications

Effect of hyperthyroidism treatments on heart rate variability : a systematic review and meta-analysis

The reversibility of HRV abnormalities in hyperthyroidism remains contradictory. The design of this study involves conducting a systematic review and meta-analysis on the effect of antithyroid treatments on HRV in hyperthyroidism. PubMed, Cochrane, Embase, and Google Scholar were searched until 4 April 2022. Multiple reviewers selected articles reporting HRV parameters in treated and untreated hyperthyroidism. Independent data extraction by multiple observers was stratified by degree of hyperthyroidism for each HRV parameter: RR intervals, SDNN (standard deviation of RR intervals), RMSSD (square root of the mean difference of successive RR intervals), pNN50 (percentage of RR intervals with >50 ms of variation), total power (TP), LFnu (low-frequency normalized unit) and HFnu (high-frequency), VLF (very low-frequency), and LF/HF ratio. We included 11 studies for a total of 471 treated hyperthyroid patients, 495 untreated hyperthyroid patients, and 781 healthy controls. After treatment, there was an increase in RR, SDNN, RMSSD, pNN50, TP, HFnu, and VLF and a decrease in LFnu and LF/HF ratio (p 0.01). Overt hyperthyroidism showed similar results, in contrast to subclinical hyperthyroidism. Compared with controls, some HRV parameter abnormalities persist in treated hyperthyroid patients (p 0.05) with lower SDNN, LFnu, and higher HFnu, without significant difference in other parameters. We showed a partial reversibility of HRV abnormalities following treatment of overt hyperthyroidism. The improvement in HRV may translate the clinical cardiovascular benefits of treatments in hyperthyroidism and may help to follow the evolution of the cardiovascular morbidity

Heart Rate Variability in Hyperthyroidism: A Systematic Review and Meta-Analysis

International audienceObjective: Cardiovascular effects of thyroid hormones may be measured through heart rate variability (HRV). We sought to determine the impact of hyperthyroidism on HRV. Design: A systematic review and meta-analysis on the impact of hyperthyroidism on HRV. Methods: PubMed, Cochrane, Embase and Google Scholar were searched until 20 August 2021 for articles reporting HRV parameters in untreated hyperthyroidism and healthy controls. Random-effects meta-analysis was stratified by degree of hyperthyroidism for each HRV parameter: RR intervals (or Normal-to-Normal intervals—NN), SDNN (standard deviation of RR intervals), RMSSD (square root of the mean difference of successive RR intervals), pNN50 (percentage of RR intervals with >50 ms of variation), total power (TP), LFnu (low-frequency normalized unit) and HFnu (high-frequency), VLF (very low-frequency), and LF/HF ratio. Results: We included 22 studies with 10,811 patients: 1002 with hyperthyroidism and 9809 healthy controls. There was a decrease in RR (effect size = −4.63, 95% CI −5.7 to −3.56), SDNN (−6.07, −7.42 to −4.71), RMSSD (−1.52, −2.18 to −0.87), pNN50 (−1.36, −1.83 to −0.88), TP (−2.05, −2.87 to −1.24), HFnu (−3.51, −4.76 to −2.26), and VLF power (−2.65, −3.74 to −1.55), and an increase in LFnu (2.66, 1.55 to 3.78) and LF/HF ratio (1.75, 1.02 to 2.48) (p < 0.01). Most parameters had ES that was twice as high in overt compared to subclinical hyperthyroidism. Increased peripheral thyroid hormones and decreased TSH levels were associated with lower RR intervals. Conclusions: Hyperthyroidism is associated with a decreased HRV, which may be explained by the deleterious effect of thyroid hormones and TSH. The increased sympathetic and decreased parasympathetic activity may have clinical implications

Does an increase in physiological indexes predict better cognitive performance: the PhyCog randomised cross-over protocol in type 2 diabetes

ProtocoleInternational audienceIntroduction There has been a growing interest towards cognitive-training programmes to improve cognition and prevent cognitive impairment despite discrepant findings. Physical activity has been recognised in maintaining or improving cognitive ability. Based on a psychoneurophysiological approach, physiological indexes should partly determine neuronal dynamics and influence cognition as any effects of cognitive training. This study’s primary aim was to examine if improved physiological indexes predict improved cognitive variables in the context of a clinical intervention programme for type 2 diabetes (T2D). Method and analysis PhyCog will be a 22-week randomised controlled trial comparing cognitive performance between three arms: (1) physical activity (1 month), a 15-day wash-out, then cognitive training (1 month), (2) cognitive training (1 month), a 15-day wash-out and physical activity (1 month), and (3) an active breathing condition (psychoeducation and resonance frequency breathing for 1 month), then a 15-day wash-out, and combined physical activity and cognitive training (1 month), allowing to determine the most effective intervention to prevent cognitive impairment associated with T2D. All participants will be observed for 3 months following the intervention. The study will include a total of 81 patients with T2D. Cognitive performance and physiological variables will be assessed at baseline (week 0—W0), during the washout (W5, 72–96 hours after week 4), at the end of the intervention (W10), and at the end of the follow-up (W22). The main variables of interest will be executive function, memory and attention. Physiological testing will involve allostatic load such as heart rate variability, microcirculation, cortisol and dehydroepiandrosterone sulfate levels. Sociodemographic and body composition will also be a consideration. Assessors will all be blinded to outcomes. To test the primary hypothesis, the relationship between improvement in physiological variables and improvement in cognitive variables (executive, memory and attention) will be collected. Ethics and dissemination This protocol was approved by the Est III French Ethics Committee (2020-A03228-31). Results will be published in peer-reviewed journals. Trial registration number NCT04915339

Cannabis Use in Physicians: A Systematic Review and Meta-Analysis

International audienceBackground: Cannabis use by physicians can be detrimental for them and their patients. We conducted a systematic review and meta-analysis on the prevalence of cannabis use by medical doctors (MDs)/students. Method: PubMed, Cochrane, Embase, PsycInfo and ScienceDirect were searched for studies reporting cannabis use in MDs/students. For each frequency of use (lifetime/past year/past month/daily), we stratified a random effect meta-analysis depending on specialties, education level, continents, and periods of time, which were further compared using meta-regressions. Results: We included 54 studies with a total of 42,936 MDs/students: 20,267 MDs, 20,063 medical students, and 1976 residents. Overall, 37% had used cannabis at least once over their lifetime, 14% over the past year, 8% over the past month and 1.1 per thousand (‰) had a daily use. Medical students had a greater cannabis use than MDs over their lifetime (38% vs. 35%, p < 0.001), the past year (24% vs. 5%, p < 0.001), and the past month (10% vs. 2%, p < 0.05), without significance for daily use (0.5% vs. 0.05%, NS). Insufficient data precluded comparisons among medical specialties. MDs/students from Asian countries seemed to have the lowest cannabis use: 16% over their lifetime, 10% in the past year, 1% in the past month, and 0.4% daily. Regarding periods of time, cannabis use seems to follow a U-shape, with a high use before 1990, followed by a decrease between 1990 and 2005, and a rebound after 2005. Younger and male MDs/students had the highest cannabis use. Conclusions: If more than a third of MDs tried cannabis at least once in their lifetime, this means its daily use is low but not uncommon (1.1‰). Medical students are the biggest cannabis users. Despite being common worldwide, cannabis use is predominant in the West, with a rebound since 2005 making salient those public health interventions during the early stage of medical studies

Impaired auditory neural performance, another dimension of hearing loss in type-2 diabetes mellitus

International audienc