International Journal of Cardiology 186 (2015) 247–249
Contents lists available at ScienceDirect
International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Letter to the editor
Sudomotor function and obesity-related risk factors in an elderly healthy population: The PROOF–Synapse Study David Hupin a, Vincent Pichot a, Sébatien Celle a, Delphine Maudoux a, Jean-Henri Calvet b, Jean-Claude Barthélémy a, Frédéric Roche a,⁎ a b
Physiologie Clinique et de l'Exercice-Centre VISAS, CHU Saint Etienne, EA SNA EPIS (EA 4706), COMUE de Lyon, UJM Saint Etienne, 42055 Saint-Etienne Cedex 2, France Impeto-Medical, 17 Rue Campagne Première, 75014 Paris, France
a r t i c l e
i n f o
Article history: Received 5 March 2015 Accepted 19 March 2015 Available online 20 March 2015 Keywords: Autonomic nervous system Obesity Fat mass index Elederly Sudoromotor function
Autonomic nervous system (ANS) dysfunction that can be assessed through alterations of sweat function is a major risk predictor of all-cause mortality in the general adult population [1]. Obesity, metabolic syndrome (MS) and obstructive sleep apnea (OSA) have been recognised as risk factors for coronary heart disease (CHD) [2,3]. Whether these factors are related with ANS dysfunction as measured by sweat function, it has never been well established. Sudoscan has been developed to provide a quick, simple, non-invasive and quantitative assessment of sweat function [4,5]. The aim of our study was to analyse the specific relationships of these different risk factors with the alterations of autonomic function measured through sweat function as screened by Sudoscan using baroreflex slope and Heart Rate Variability (HRV) as reference for ANS activity. The PROgnostic indicator OF cardiovascular and cerebrovascular events (PROOF) study [6] is a prospective longitudinal cohort study recruited amongst the inhabitants of the city of Saint Etienne, France and older than 65 years. Subjects with prior cardiac events such as myocardial infarction and congestive heart disease, with type-1 diabetes, dependent people or people living in institutions, were excluded from the study. The PROOF study was approved by the IRB-IEC (CCPRB Rhone-Alpes Loire). All subjects signed an informed consent for the study. ⁎ Corresponding author at: Centre VISAS, CHU Nord Niveau 3-Batiement A, F-42055 Saint-Etienne Cedex 2, France. E-mail address: [email protected] (F. Roche).
http://dx.doi.org/10.1016/j.ijcard.2015.03.273 0167-5273/© 2015 Published by Elsevier Ireland Ltd.
Body Fat Index (BFI) used to evaluate body fat repartition (lean mass divided by the squared height in metres) was evaluated by Dual Energy X-Ray Absorptiometry (DEXA, Delphi WS/N70453) and classified into tertiles using the following thresholds expressed in kg/m2 normal (b6.9), moderately elevated (6.9–9.3) and high (N9.3 kg/m2). Heart Rate Variability (HRV) was measured from 24-hour ECG Holter monitoring (Vista, Novacor, Rueil-Malmaison, France). Each RR interval was manually validated before analysis and log transformed frequencydomain HRV variables (Fourier transform) were calculated. Spontaneous cardiac baroreflex activity was calculated over 15 min, at rest in the supine position. Finger arterial blood pressure was measured by the volume-clamp method by means of a non-invasive continuous blood pressure monitor (Finapres 2300, Ohmeda ®). The spontaneous cardiac baroreflex activity (expressed in milliseconds per millimetre of Hg) was calculated as the mean of the slopes of at least three or more successive heart beats in which there were concordant increases and decreases in systolic blood pressure. A nocturnal unattended sleep study was performed at home in all subjects using a polygraphic system (HypnoPTT; Tyco Healthcare, Puritan Bennett, Boulder, CO, USA). Arterial oxygen saturation was measured by pulse oximetry (SaO2). A recording was considered acceptable if ≥5 h of recording without missing data on respiratory signals and SaO2 was obtained. A second night of monitoring was performed when subjective sleep latency exceeded 2 h, sleep duration was b 5 h, or respiratory recording was considered not acceptable for more than 40% of the total recording time. All recordings were visually validated and manually scored for respiratory events and nocturnal SaO2, according to standard criteria with an intra-scorer reliability of 88%. Hypopnea was defined as ≥50% reduction in airflow from baseline value lasting ≥10 s associated with ≥3% oxygen desaturation. Apneas were defined as the absence of airflow on the nasal cannula lasting for ≥10 s. Indices of nocturnal hypoxemia were the following: mean SaO2, percentage of recording time below 90%, and minimal SaO2 value recorded during sleep and the oxygen desaturation index (ODI), i.e., the number of episodes of oxygen desaturation per hour of recording time during which blood oxygen fell by 3% or more. For measurement of sweat function using Sudoscan patients were asked to put their hands and feet on stainless–steel electrodes during 2 min and electrochemical Skin Conductances (ESC expressed in μS) representing local sweat function were computed. A cardiac risk score based on conductances and demographic data was immediately available. Patients were classified according to cardiac risk values. Stepwise logistic regression
248
D. Hupin et al. / International Journal of Cardiology 186 (2015) 247–249
was performed to analyse the interrelations between the same parameters. Results were considered as significant for p values less than 0.05. In this representative subgroup of the PROOF study population (n = 376, mean age 74.8 ± 1.0 years, 46% men, BMI: 26.6 ± 3.9 kg/m2) all variables recognised as risk factors but ODI and SaO2 mean, and BRS and HRV variables were significantly different according to Sudoscan cardiac risk score (Table 1). Univariate and multivariate logistic regression analyses between Sudoscan risk score and different risk factors or BRS and HRV are displayed in Table 2. When looking at the effect of BMI alone in this population with homogenous age the Odds Ratios (ORs) for BFI and FPG were 1.68 (1.48–1.92) and 1.14 (1.04–1.24) respectively for each unit of BMI. In addition stepwise logistic regression analysis showed that BFI was the main factor predicting Sudoscan risk score. Multi-adjusted logistic regression evidenced that OR of BFI ≥9.3 vs b9.3 was 14.06 (4.84–40.89) p b 0.0001 for having CAN N 50, while it reached 4.74 (1.96–11.49): p = 0.026 for FPG ≥1.1 vs b 1.1 and 2.54 (1.11–5.85): p = 0.0279 for HRV total power spectral density b384 vs ≥384 (median of the population). In this elderly population BFI shows the strongest correlation with Sudoscan risk score. It is in accordance with previous studies evidencing that sudomotor dysfunction was linked to metabolic syndrome and that it can improve with increased daily physical activity reducing cardiac risk [7,8]. The link between Sudoscan and HRV or BRS confirms previous studies that evidenced a correlation between Sudoscan and Ewing tests or HRV analysis, confirming that sudomotor dysfunction could be used
as a screening tool for global ANS desequilibrium [9]. These results together are in accordance with previous studies showing that BFI is linked to the ANS especially the sympathetic nervous system [10]. No link with obstructive sleep apnea and hypoxemic load could be demonstrated, in a population suffering middle severity of the sleep related disorder. This study, to be confirmed on a larger and younger population, shows that Sudoscan, which is non-invasive and quantitative, could be used as a screening tool and follow-up for risk factors before performance of more specific but also more time consuming tests. Conflict of interest The authors report no relationships that could be construed as a conflict of interest. Acknowledgements The University Hospital Saint-Etienne and the institutional review board (Comité Consultatif de Protection des Personnes dans la Recherche Biomédicale Rhône-Alpes Loire) approved the PROOF study. The National Committee for Information and Liberty provided consent for the data collection. All subjects provided written consent for study participation.
Table 1 Distribution of anthropometric, biological and polygraphic data according to Sudoscan cardiac risk score.
Hand ESC (μS) Feet ESC (μS) Body Fat mass Index (kg/m2) Fasting plasma Glucose (g/l) Metabolic syndrome, n (%) BRS slope (ms/mm Hg) ODI (n/h) SaO2 mean (%) SaO2 minimal (%) Time SaO2 b90 (%) HRV total power (ms2)
Sudoscan cardiac risk score b40 (n = 139)
Sudoscan cardiac risk score 40–50 (n = 189)
Sudoscan cardiac risk score ≥50 (n = 48)
p value⁎
73.0 (65.0–79.5) 84.0 (81.0–87.0) 6.5 (5.3–7.9) 0.9 (0.9–1.0) 7 (8.7) 6.8 (5.5–8.9) 6.2 (3.4–10.4) 95.0 (94.0–96.0) 91.0 (89.0–92.0) 0.0 (0.0–1.0) 406.0 (258.0–827.0)
66.0 (58.0–75.0) 75.5 (69.0–81.0) 9.0 (7.9–10.7) 0.9 (0.9–1.0) 8 (7.0) 6.4 (5.0–8.4) 8.3 (3.7–16.2) 95.0 (94.0–96.0) 89.0 (86.0–92.0) 0.1 (0.0–2.0) 374.5 (187.0–684.0)
61.0 (52.0–70.5) 68.0 (59.0–75.0) 12.4 (10.5–14.9) 1.0 (0.9–1.2) 11 (32.4) 5.6 (4.6–7.4) 8.1 (4.5–14.3) 95.0 (93.0–95.0) 89.0 (86.0–91.0) 0.2 (0.0–1.9) 288.0 (159.0–495.0)
b0.0001 b0.0001 b0.0001 0.0004 0.0001 0.0492 0.5226 0.9809 0.013 0.011 0.0339
Data are median (Q1–Q3). ⁎ p value of Mood's test for median comparison or χ2 for Metabolic syndrome.
Table 2 Univariate and multivariate logistic regression analyses between Sudoscan cardiac risk score and anthropometric, biological and polygraphic data.
Metabolic syndrome BFI (kg/m2) Fasting plasma glucose (g/l) ODI (n/h) BRS slope (ms/mm Hg) SaO2 mean (%) SaO2 minimal (%) Time SaO2 b90 (%) HRV total power (ms2)
Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
Sudoscan cardiac risk score b40 (n = 138)
Sudoscan cardiac risk score 40–50 (n = 189)
Sudoscan cardiac risk score ≥50 (n = 48)
p for trend
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
0.79 (0.27–2.27) 0.78 (0.27–2.24) 6.13 (3.07–12.3) 7.38 (3.50–15.6) 0.86 (0.39–1.87) 0.87 (0.40–1.91) 1.39 (0.79–2.43) 1.48 (0.84–2.64) 1.28 (0.80–2.04) 1.27 (0.79–2.02) 1.98 (1.10–3.57) 2.06 (1.14–3.75) 2.30 (1.28–4.14) 2.34 (1.30–4.22) 2.21 (1.23–3.97) 2.25 (1.25–4.04) 1.33 (0.83–2.14) 1.33 (0.83–2.14)
5.06 (1.76–14.6) 4.90 (1.70–14.2) 32.9 (11.2–96.1) 63.9 (18.3–223) 3.71 (1.56–8.81) 4.02 (1.66–9.71) 1.20 (0.47–3.04) 1.34 (0.52–3.48) 2.06 (1.03–4.15) 2.02 (1.00–4.07) 1.72 (0.65–4.57) 1.87 (0.70–5.03) 2.90 (1.10–7.69) 3.01 (1.13–8.04) 2.95 (1.12–7.81) 3.05 (1.15–8.13) 2.34 (1.13–4.84) 2.34 (1.13–4.85)
0.004 0.0044 b0.0001 b0.0001 0.0013 0.0011 NS NS 0.0127 0.0137 0.004 0.003 0.0005 0.0005 0.0004 0.0004 0.0292 0.0298
Model 1 is unadjusted. Model 2 is adjusted on gender. Cases are defined as follows: metabolic syndrome yes/no, BFI ≥9.3 kg/m2, fasting plasma glucose N1.1 g/l, ODI b7.55 n/h (median), BRS slope b6 mm Hg, SaO2 mean b95% (median), SaO2 minimal b90% (median), Time SaO2 b 90 ≤ 0% (median), HRV total power b384 ms2 (median).
D. Hupin et al. / International Journal of Cardiology 186 (2015) 247–249
This study was supported by a grant from the French Ministry of Health (Cellule Projet Hospitalier de Recherche Clinique National, Direction de la Recherche Clinique, CHU Saint-Etienne; Appel d'Offre 1998 and Appel d'Offre 2002) and by a grant from L'Association de Recherche SYNAPSE (President: Michel Segura).
References [1] H. Tsuji, M.G. Larson, F.J. Venditti Jr., et al., Impact of reduced heart rate variability on risk for cardiac events. The Framingham Heart Study, Circulation 94 (11) (1996) 2850–2855. [2] J. Sztajzel, A. Golay, V. Makoundou, et al., Impact of body fat mass extent on cardiac autonomic alterations in women, Eur. J. Clin. Invest. 39 (8) (2009) 649–656. [3] W.T. McNicholas, M.R. Bonsignore, Sleep apnoea as an independent risk factor for cardiovascular disease: current evidence, basic mechanisms and research priorities, Eur. Respir. J. 29 (2007) 156–178. [4] C.M. Casellini, H.K. Parson, M.S. Richardson, M.L. Nevoret, A.I. Vinik, SUDOSCAN, a non-invasive tool for detecting diabetic small fiber neuropathy and autonomic dysfunction, Diabetes Technol. Ther. 15 (11) (2013) 948–953.
249
[5] B.I. Freedman, D.W. Bowden, S.C. Smith, J. Xu, J. Divers, Relationships between electrochemical skin conductance and kidney disease in Type 2 diabetes, J. Diabet. Complications 28 (1) (2014) 56–60. [6] J.C. Barthelemy, V. Pichot, V. Dauphinot, et al., Autonomic nervous system activity and decline as prognostic indicators of cardiovascular and cerebrovascular events: the ‘PROOF’ Study. Study design and population sample. Associations with sleeprelated breathing disorders: the ‘SYNAPSE’ Study, Neuroepidemiology 29 (1–2) (2007) 18–28. [7] K. Sun, Y. Liu, M. Dai, M. Li, Z. Yang, M. Xu, Y. Xu, J. Lu, Y. Chen, J. Liu, G. Ning, Y. Bi, Accessing autonomic function can early screen metabolic syndrome, Plos One 7 (8) (2012). [8] A. Raisanen, J. Eklund, J.H. Calvet, J. Tuomilehto, Sudomotor function as a tool for cardiorespiratory fitness level evaluation: comparison with maximal exercise capacity, Int. J. Environ. Res. Public Health 11 (2014) 5839–5848. [9] C.S. Yajnik, V. Kantikar, A. Pande, J.P. Deslypere, J. Dupin, J.H. Calvet, B. Bauduceau, Screening of cardiovascular autonomic neuropathy in patients with diabetes using non-invasive quick and simple assessment of sudomotor function, Diabetes Metab. 39 (2) (2013) 126–131. [10] H.G. Ntougou Assoumou, V. Pichot, J.C. Barthelemy, V. Dauphinot, S. Celle, P. Collet, J.M. Gaspoz, F. Roche, Obesity-related autonomic nervous system disorders are best associated with body fat mass index, a new indicator, Int. J. Cardiol. 153 (1) (2011 Nov 17) 111–113.
Contents lists available at ScienceDirect
International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Letter to the editor
Sudomotor function and obesity-related risk factors in an elderly healthy population: The PROOF–Synapse Study David Hupin a, Vincent Pichot a, Sébatien Celle a, Delphine Maudoux a, Jean-Henri Calvet b, Jean-Claude Barthélémy a, Frédéric Roche a,⁎ a b
Physiologie Clinique et de l'Exercice-Centre VISAS, CHU Saint Etienne, EA SNA EPIS (EA 4706), COMUE de Lyon, UJM Saint Etienne, 42055 Saint-Etienne Cedex 2, France Impeto-Medical, 17 Rue Campagne Première, 75014 Paris, France
a r t i c l e
i n f o
Article history: Received 5 March 2015 Accepted 19 March 2015 Available online 20 March 2015 Keywords: Autonomic nervous system Obesity Fat mass index Elederly Sudoromotor function
Autonomic nervous system (ANS) dysfunction that can be assessed through alterations of sweat function is a major risk predictor of all-cause mortality in the general adult population [1]. Obesity, metabolic syndrome (MS) and obstructive sleep apnea (OSA) have been recognised as risk factors for coronary heart disease (CHD) [2,3]. Whether these factors are related with ANS dysfunction as measured by sweat function, it has never been well established. Sudoscan has been developed to provide a quick, simple, non-invasive and quantitative assessment of sweat function [4,5]. The aim of our study was to analyse the specific relationships of these different risk factors with the alterations of autonomic function measured through sweat function as screened by Sudoscan using baroreflex slope and Heart Rate Variability (HRV) as reference for ANS activity. The PROgnostic indicator OF cardiovascular and cerebrovascular events (PROOF) study [6] is a prospective longitudinal cohort study recruited amongst the inhabitants of the city of Saint Etienne, France and older than 65 years. Subjects with prior cardiac events such as myocardial infarction and congestive heart disease, with type-1 diabetes, dependent people or people living in institutions, were excluded from the study. The PROOF study was approved by the IRB-IEC (CCPRB Rhone-Alpes Loire). All subjects signed an informed consent for the study. ⁎ Corresponding author at: Centre VISAS, CHU Nord Niveau 3-Batiement A, F-42055 Saint-Etienne Cedex 2, France. E-mail address: [email protected] (F. Roche).
http://dx.doi.org/10.1016/j.ijcard.2015.03.273 0167-5273/© 2015 Published by Elsevier Ireland Ltd.
Body Fat Index (BFI) used to evaluate body fat repartition (lean mass divided by the squared height in metres) was evaluated by Dual Energy X-Ray Absorptiometry (DEXA, Delphi WS/N70453) and classified into tertiles using the following thresholds expressed in kg/m2 normal (b6.9), moderately elevated (6.9–9.3) and high (N9.3 kg/m2). Heart Rate Variability (HRV) was measured from 24-hour ECG Holter monitoring (Vista, Novacor, Rueil-Malmaison, France). Each RR interval was manually validated before analysis and log transformed frequencydomain HRV variables (Fourier transform) were calculated. Spontaneous cardiac baroreflex activity was calculated over 15 min, at rest in the supine position. Finger arterial blood pressure was measured by the volume-clamp method by means of a non-invasive continuous blood pressure monitor (Finapres 2300, Ohmeda ®). The spontaneous cardiac baroreflex activity (expressed in milliseconds per millimetre of Hg) was calculated as the mean of the slopes of at least three or more successive heart beats in which there were concordant increases and decreases in systolic blood pressure. A nocturnal unattended sleep study was performed at home in all subjects using a polygraphic system (HypnoPTT; Tyco Healthcare, Puritan Bennett, Boulder, CO, USA). Arterial oxygen saturation was measured by pulse oximetry (SaO2). A recording was considered acceptable if ≥5 h of recording without missing data on respiratory signals and SaO2 was obtained. A second night of monitoring was performed when subjective sleep latency exceeded 2 h, sleep duration was b 5 h, or respiratory recording was considered not acceptable for more than 40% of the total recording time. All recordings were visually validated and manually scored for respiratory events and nocturnal SaO2, according to standard criteria with an intra-scorer reliability of 88%. Hypopnea was defined as ≥50% reduction in airflow from baseline value lasting ≥10 s associated with ≥3% oxygen desaturation. Apneas were defined as the absence of airflow on the nasal cannula lasting for ≥10 s. Indices of nocturnal hypoxemia were the following: mean SaO2, percentage of recording time below 90%, and minimal SaO2 value recorded during sleep and the oxygen desaturation index (ODI), i.e., the number of episodes of oxygen desaturation per hour of recording time during which blood oxygen fell by 3% or more. For measurement of sweat function using Sudoscan patients were asked to put their hands and feet on stainless–steel electrodes during 2 min and electrochemical Skin Conductances (ESC expressed in μS) representing local sweat function were computed. A cardiac risk score based on conductances and demographic data was immediately available. Patients were classified according to cardiac risk values. Stepwise logistic regression
248
D. Hupin et al. / International Journal of Cardiology 186 (2015) 247–249
was performed to analyse the interrelations between the same parameters. Results were considered as significant for p values less than 0.05. In this representative subgroup of the PROOF study population (n = 376, mean age 74.8 ± 1.0 years, 46% men, BMI: 26.6 ± 3.9 kg/m2) all variables recognised as risk factors but ODI and SaO2 mean, and BRS and HRV variables were significantly different according to Sudoscan cardiac risk score (Table 1). Univariate and multivariate logistic regression analyses between Sudoscan risk score and different risk factors or BRS and HRV are displayed in Table 2. When looking at the effect of BMI alone in this population with homogenous age the Odds Ratios (ORs) for BFI and FPG were 1.68 (1.48–1.92) and 1.14 (1.04–1.24) respectively for each unit of BMI. In addition stepwise logistic regression analysis showed that BFI was the main factor predicting Sudoscan risk score. Multi-adjusted logistic regression evidenced that OR of BFI ≥9.3 vs b9.3 was 14.06 (4.84–40.89) p b 0.0001 for having CAN N 50, while it reached 4.74 (1.96–11.49): p = 0.026 for FPG ≥1.1 vs b 1.1 and 2.54 (1.11–5.85): p = 0.0279 for HRV total power spectral density b384 vs ≥384 (median of the population). In this elderly population BFI shows the strongest correlation with Sudoscan risk score. It is in accordance with previous studies evidencing that sudomotor dysfunction was linked to metabolic syndrome and that it can improve with increased daily physical activity reducing cardiac risk [7,8]. The link between Sudoscan and HRV or BRS confirms previous studies that evidenced a correlation between Sudoscan and Ewing tests or HRV analysis, confirming that sudomotor dysfunction could be used
as a screening tool for global ANS desequilibrium [9]. These results together are in accordance with previous studies showing that BFI is linked to the ANS especially the sympathetic nervous system [10]. No link with obstructive sleep apnea and hypoxemic load could be demonstrated, in a population suffering middle severity of the sleep related disorder. This study, to be confirmed on a larger and younger population, shows that Sudoscan, which is non-invasive and quantitative, could be used as a screening tool and follow-up for risk factors before performance of more specific but also more time consuming tests. Conflict of interest The authors report no relationships that could be construed as a conflict of interest. Acknowledgements The University Hospital Saint-Etienne and the institutional review board (Comité Consultatif de Protection des Personnes dans la Recherche Biomédicale Rhône-Alpes Loire) approved the PROOF study. The National Committee for Information and Liberty provided consent for the data collection. All subjects provided written consent for study participation.
Table 1 Distribution of anthropometric, biological and polygraphic data according to Sudoscan cardiac risk score.
Hand ESC (μS) Feet ESC (μS) Body Fat mass Index (kg/m2) Fasting plasma Glucose (g/l) Metabolic syndrome, n (%) BRS slope (ms/mm Hg) ODI (n/h) SaO2 mean (%) SaO2 minimal (%) Time SaO2 b90 (%) HRV total power (ms2)
Sudoscan cardiac risk score b40 (n = 139)
Sudoscan cardiac risk score 40–50 (n = 189)
Sudoscan cardiac risk score ≥50 (n = 48)
p value⁎
73.0 (65.0–79.5) 84.0 (81.0–87.0) 6.5 (5.3–7.9) 0.9 (0.9–1.0) 7 (8.7) 6.8 (5.5–8.9) 6.2 (3.4–10.4) 95.0 (94.0–96.0) 91.0 (89.0–92.0) 0.0 (0.0–1.0) 406.0 (258.0–827.0)
66.0 (58.0–75.0) 75.5 (69.0–81.0) 9.0 (7.9–10.7) 0.9 (0.9–1.0) 8 (7.0) 6.4 (5.0–8.4) 8.3 (3.7–16.2) 95.0 (94.0–96.0) 89.0 (86.0–92.0) 0.1 (0.0–2.0) 374.5 (187.0–684.0)
61.0 (52.0–70.5) 68.0 (59.0–75.0) 12.4 (10.5–14.9) 1.0 (0.9–1.2) 11 (32.4) 5.6 (4.6–7.4) 8.1 (4.5–14.3) 95.0 (93.0–95.0) 89.0 (86.0–91.0) 0.2 (0.0–1.9) 288.0 (159.0–495.0)
b0.0001 b0.0001 b0.0001 0.0004 0.0001 0.0492 0.5226 0.9809 0.013 0.011 0.0339
Data are median (Q1–Q3). ⁎ p value of Mood's test for median comparison or χ2 for Metabolic syndrome.
Table 2 Univariate and multivariate logistic regression analyses between Sudoscan cardiac risk score and anthropometric, biological and polygraphic data.
Metabolic syndrome BFI (kg/m2) Fasting plasma glucose (g/l) ODI (n/h) BRS slope (ms/mm Hg) SaO2 mean (%) SaO2 minimal (%) Time SaO2 b90 (%) HRV total power (ms2)
Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
Sudoscan cardiac risk score b40 (n = 138)
Sudoscan cardiac risk score 40–50 (n = 189)
Sudoscan cardiac risk score ≥50 (n = 48)
p for trend
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
0.79 (0.27–2.27) 0.78 (0.27–2.24) 6.13 (3.07–12.3) 7.38 (3.50–15.6) 0.86 (0.39–1.87) 0.87 (0.40–1.91) 1.39 (0.79–2.43) 1.48 (0.84–2.64) 1.28 (0.80–2.04) 1.27 (0.79–2.02) 1.98 (1.10–3.57) 2.06 (1.14–3.75) 2.30 (1.28–4.14) 2.34 (1.30–4.22) 2.21 (1.23–3.97) 2.25 (1.25–4.04) 1.33 (0.83–2.14) 1.33 (0.83–2.14)
5.06 (1.76–14.6) 4.90 (1.70–14.2) 32.9 (11.2–96.1) 63.9 (18.3–223) 3.71 (1.56–8.81) 4.02 (1.66–9.71) 1.20 (0.47–3.04) 1.34 (0.52–3.48) 2.06 (1.03–4.15) 2.02 (1.00–4.07) 1.72 (0.65–4.57) 1.87 (0.70–5.03) 2.90 (1.10–7.69) 3.01 (1.13–8.04) 2.95 (1.12–7.81) 3.05 (1.15–8.13) 2.34 (1.13–4.84) 2.34 (1.13–4.85)
0.004 0.0044 b0.0001 b0.0001 0.0013 0.0011 NS NS 0.0127 0.0137 0.004 0.003 0.0005 0.0005 0.0004 0.0004 0.0292 0.0298
Model 1 is unadjusted. Model 2 is adjusted on gender. Cases are defined as follows: metabolic syndrome yes/no, BFI ≥9.3 kg/m2, fasting plasma glucose N1.1 g/l, ODI b7.55 n/h (median), BRS slope b6 mm Hg, SaO2 mean b95% (median), SaO2 minimal b90% (median), Time SaO2 b 90 ≤ 0% (median), HRV total power b384 ms2 (median).
D. Hupin et al. / International Journal of Cardiology 186 (2015) 247–249
This study was supported by a grant from the French Ministry of Health (Cellule Projet Hospitalier de Recherche Clinique National, Direction de la Recherche Clinique, CHU Saint-Etienne; Appel d'Offre 1998 and Appel d'Offre 2002) and by a grant from L'Association de Recherche SYNAPSE (President: Michel Segura).
References [1] H. Tsuji, M.G. Larson, F.J. Venditti Jr., et al., Impact of reduced heart rate variability on risk for cardiac events. The Framingham Heart Study, Circulation 94 (11) (1996) 2850–2855. [2] J. Sztajzel, A. Golay, V. Makoundou, et al., Impact of body fat mass extent on cardiac autonomic alterations in women, Eur. J. Clin. Invest. 39 (8) (2009) 649–656. [3] W.T. McNicholas, M.R. Bonsignore, Sleep apnoea as an independent risk factor for cardiovascular disease: current evidence, basic mechanisms and research priorities, Eur. Respir. J. 29 (2007) 156–178. [4] C.M. Casellini, H.K. Parson, M.S. Richardson, M.L. Nevoret, A.I. Vinik, SUDOSCAN, a non-invasive tool for detecting diabetic small fiber neuropathy and autonomic dysfunction, Diabetes Technol. Ther. 15 (11) (2013) 948–953.
249
[5] B.I. Freedman, D.W. Bowden, S.C. Smith, J. Xu, J. Divers, Relationships between electrochemical skin conductance and kidney disease in Type 2 diabetes, J. Diabet. Complications 28 (1) (2014) 56–60. [6] J.C. Barthelemy, V. Pichot, V. Dauphinot, et al., Autonomic nervous system activity and decline as prognostic indicators of cardiovascular and cerebrovascular events: the ‘PROOF’ Study. Study design and population sample. Associations with sleeprelated breathing disorders: the ‘SYNAPSE’ Study, Neuroepidemiology 29 (1–2) (2007) 18–28. [7] K. Sun, Y. Liu, M. Dai, M. Li, Z. Yang, M. Xu, Y. Xu, J. Lu, Y. Chen, J. Liu, G. Ning, Y. Bi, Accessing autonomic function can early screen metabolic syndrome, Plos One 7 (8) (2012). [8] A. Raisanen, J. Eklund, J.H. Calvet, J. Tuomilehto, Sudomotor function as a tool for cardiorespiratory fitness level evaluation: comparison with maximal exercise capacity, Int. J. Environ. Res. Public Health 11 (2014) 5839–5848. [9] C.S. Yajnik, V. Kantikar, A. Pande, J.P. Deslypere, J. Dupin, J.H. Calvet, B. Bauduceau, Screening of cardiovascular autonomic neuropathy in patients with diabetes using non-invasive quick and simple assessment of sudomotor function, Diabetes Metab. 39 (2) (2013) 126–131. [10] H.G. Ntougou Assoumou, V. Pichot, J.C. Barthelemy, V. Dauphinot, S. Celle, P. Collet, J.M. Gaspoz, F. Roche, Obesity-related autonomic nervous system disorders are best associated with body fat mass index, a new indicator, Int. J. Cardiol. 153 (1) (2011 Nov 17) 111–113.
