Usefulness of Truncal Obesity Indices as Predictive Factors for Obstructive Sleep Apnea Syndrome

Martínez‐Rivera, Carlos; Abad, Jorge; Fiz, J.A.; Ríos, José; Morera, Josep

doi:10.1038/oby.2007.20

Cited by 85 publications

(73 citation statements)

References 33 publications

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“…For example, in men, the odds ratio for a 5-year increase in AHI of X15 with a gain of at least 10 kg was 5.2, but the odds ratio for a loss in AHI of at least 15 with a loss of X10 kg was 2.9. Martinez-Rivera et al (2008) compared different anthropometric indicators in relation to obstructive sleep apnoea syndrome (AHI 4 10). In a multiple logistic regression model, BMI (obese vs non-obese) was not associated with OSAS but a high waist-hip ratio (cut-points 1 for men and 0.85 for women) was associated with an odds ratio of 2.6 (1.2-5.8).…”

Section: Sleep Apnoeamentioning

confidence: 99%

Waist circumference and waist/hip ratio in relation to all-cause mortality, cancer and sleep apnea

2009

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Abdominal obesity assessed by waist or waist/hip ratio are both related to increased risk of all-cause mortality throughout the range of body mass index (BMI). The relative risks (RRs) seem to be relatively stronger in younger than in older adults and in those with relatively low BMI compared with those with high BMI. Absolute risks and risk differences are preferable measures of risk in a public health context but these are rarely presented. There is a great lack of studies in ethnic groups (groups of African and Asian descent particularly). Current cut-points as recommended by the World Health Organization seem appropriate, although it may be that BMI-specific and ethnic-specific waist cut-points may be warranted. Waist alone could replace both waist-hip ratio and BMI as a single risk factor for all-cause mortality. There is much less evidence for waist to replace BMI for cancer risk mainly because of the relative lack of prospective cohort studies on waist and cancer risk. Obesity is also a risk factor for sleep apnoea where neck circumference seems to give the strongest association, and waist-hip ratio is a risk factor especially in severe obstructive sleep apnoea syndrome. The waist circumference and waist-hip ratio seem to be better indicators of all-cause mortality than BMI. Waist circumference, waist-hip ratio and all-cause mortalityWhat is the optimal cut-point for assessing risk of premature death?-purposes of cut-points There are not very many studies that directly compare mortality risks by waist circumference and waist-hip ratio. Those who do usually show a continuous relationship between waist, waist/hip and mortality, making any decision about cut-points and classification rather arbitrary. It is also important to think about the uses for cut-points (WHO Expert Consultation, 2004). Cut-points can be applied to population data to describe prevalence and to provide information that may trigger policy actions, to facilitate prevention programmes and to measure the effect of interventions. They can also be used in epidemiological studies to describe the strength and effect size of relationships of determinants on health outcomes. Finally, they can be used to identify individuals who are at risk, to determine the type and intensity of treatment and to evaluate the effects of treatment.The focus of this paper is to evaluate the nature and form of the relationships between waist, waist/hip and all-cause mortality in prospective epidemiological studies and to identify potential cut-points based on for example, statistical grounds (e.g. quintiles), the flexing of the curves or of absolute and relative risk (RR) estimates.The evaluation of the impact of anthropometric measures on mortality can be done by calculating absolute risks (and rate or risk differences), RR or population attributable risk. The RR gives an impression of the slope of the association but has the disadvantage of being dependent on the level of background risk. The risk difference gives the best assessment of the impact on mortality but...

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Section: Sleep Apnoeamentioning

confidence: 99%

Waist circumference and waist/hip ratio in relation to all-cause mortality, cancer and sleep apnea

2009

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“…19 These informations consolidate the assumption that BMI is not a beneficial estimation factor of OSA, whether abdominal fat indices is better susceptible factor for estimation. [25][26][27] In conclusion, our findings indicate significantly higher values for thoracic periaortic adipose tissue in OSA than controls, being associated positively with AHI levels and strongly predicted by AHI levels in OSA subjects, while not differing with respect to gender and smoking status.…”

Section: Discussionmentioning

confidence: 62%

Relationship Between Obstructive Sleep Apnea and Increased Thoracic Periaortic Adipose Tissue

Akyürek¹,

Karamanli²,

Efe³

2016

Turkiye Klinikleri J Intern Med

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A AB BS S TTR RA AC CT T O Ob bj je ec ct ti iv ve e: : Aim of this study is to demonstrate the increased thoracic periaortic adipose tissue volume which is related to increased cardiovascular risk in subjects with obstructive sleep apnea (OSA) compared to control group. M Ma at te er ri ia al l a an nd d M Me et th ho od ds s: : The study population consisted of 25 newly diagnosed OSA patients (mean (SD) age: 55.0 (±12.1) years, 64% were males) and 34 healthy volunteers (mean (SD) age: 53.2 (±11.8) years, 61.8% were males). Standard overnight polysomnography system had applied to all of the participant and apnea-hypopnea index (AHI) was calculated. In addition, all of the participants underwent thoracic radiographic assessment in the supine position, using an 8-slice multidetector computed tomography scanner and thoracic periaortic adipose tissue volume was measured. [204.7 (±39.6) mg/dL vs. 183.5 (±36.1) (p=0.041)] levels were significantly higher compared to the control group. While there was a positive correlation between thoracic periaortic adipose tissue and AHI, no correlation was found with the other parameters. In stepwise regression analysis, AHI emerged as a significant predictor of thoracic periaortic adipose tissue (r= 0.41, p= 0.038), contributing to 13.7% of its variability. In OSA subjects, no significant difference was noted in thoracic periaortic adipose tissue levels with respect to gender (p=0.72), total cholesterol (p= 0.53), triglyceride (p= 0.34), or smoking status (p=0, 32). C Co on nc cl lu us si io on n: : Our findings indicate significantly higher values for thoracic periaortic adipose tissue in OSA than controls, being associated positively with dyslipidemia and strongly predicted by AHI levels in OSA subjects, while not differing with respect to gender, and smoking status. [204,7 (±39,6) mg/dL vs. 183,5 (±36,1) (p=0,041)] düzeyleri kontrol grubuna göre anlamlı derecede yüksek bulundu. TAT ile AHI (r=0,441; p=0,027) arasında pozitif korelasyon varken diğer parametrelerle korelasyon saptanamadı. Aşamalı regresyon analizinde AHI TAT için önemli bir belirteç olarak ortaya çıktı (β= 0,414, p= 0,038). OSA hastalarında TAT düzeyi cinsiyet (p=0,728) ya da sigara içiciliği durumu (p=320) açısından farklılık göstermedi. S So on nu uç ç: : Bizim bulgularımız incelendiğinde OSA hastalarında TAT volümü kontrol grubuna göre önemli derecede yüksekti ve dislipidemi ile pozitif korelasyonu vardı. OSA hastalarında TAT için AHI düzeyi güçlü bir belirleyici iken cinsiyet ve sigara içiciliği yönünden fark göstermedi.

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“…La relación de la obesidad abdominal con el SM, al ser un criterio necesario para su diagnóstico si seguimos la definición de la IDF, está clara; si bien, existe controversia en la literatura de si es el IMC o el perímetro abdominal el mejor factor predictor de la aparición del SAHS. Nuestros datos sugieren que el acú-mulo de grasa visceral es un factor de riesgo importante para la presencia de SAHS; hallazgos similares al nuestro aparecen en otras series 21,25,26 , mientras que en otros estudios es el IMC el factor relacionado con la presencia de SAHS 22 . Este estudio puede tener importantes aplicaciones prácticas.…”

Section: Discussionunclassified

Síndrome metabólico y sus componentes en pacientes con síndrome de apnea-hipoapnea del sueño

Tumbeiro

Acuna

Alves

et al. 2011

Anales Sis San Navarra

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Fundamento. El síndrome de apnea-hipoapnea del sueño (SAHS) es un factor de riesgo reconocido de patología cardiovascular y la evidencia reciente sugiere que está relacionado con el síndrome metabólico (SM). El objetivo del estudio fue conocer la prevalencia de los componentes individuales del SM y del SM como entidad, en pacientes con sospecha clínica de SAHS, y ver si ambas entidades están en relación con la obesidad central. Material y métodos.Se estudiaron prospectivamente a 486 pacientes consecutivos, 359 hombres (73,9%), con una edad media de 57,3 ± 13,5 años y un índice de masa corporal (IMC) de 32,1 ± 6,5 kg/m2 con sospecha clínica de SAHS. El SAHS se diagnosticó por registro nocturno. Un Índice de Apnea-Hipoapnea (IAH) > 5 era considerado diagnóstico de SAHS. El SM era diagnosticado siguiendo los criterios de la International Diabetes Federation.resultados. De los pacientes estudiados 325 (66,9%) eran SAHS de grado moderado-grave. El IAH medio fue de 30,2 ± 23,8. De 456 pacientes (93,8%) teníamos datos para el diagnóstico de SM y su prevalencia fue del 64,7% (295 pacientes). En el análisis multivariante, tanto la edad como el perímetro abdominal se comportaron como predictores de SAHS y de SM (p<0,05).conclusiones. El SM es más frecuente en pacientes con SAHS, y su prevalencia aumenta con su gravedad. La obesidad central, medida por el perímetro abdominal, resultó ser un factor predictor tanto de SAHS como de SM. Methods.We examined the records of 486 consecutive patients, 359 (73.9%) men, with a mean age of 57.3 ± 13.5 years and a mean body mass index (BMI) of 32.1 ± 6.5 kg/m2), with clinical suspicion of SAHS. SAHS was diagnosed from the nocturnal register. An Apnea-Hipopnea Index (AHI) > 5 was considered SAHS. MS was evaluated according to the diagnostic criteria of the International Diabetes Federation.results. Three hundred and twenty-five (66.9%) of the entire group were moderate-severe SAHS. The mean of AHI was 30.2 ± 23.8. We had sufficient data available on 456 patients (93.8%) for MS diagnosis and its prevalence was 64.7% (295 patients). Multivariate analysis showed that age and abdominal perimeter were predictors of SAHS and MS (p<0.05).conclusions. Patients with SAHS have a high prevalence of MS and their prevalence is greater in patients with major levels of SAHS. The rate of central obesity, measured by abdominal perimeter, predicts SAHS and MS.

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Usefulness of Truncal Obesity Indices as Predictive Factors for Obstructive Sleep Apnea Syndrome

Cited by 85 publications

References 33 publications

Waist circumference and waist/hip ratio in relation to all-cause mortality, cancer and sleep apnea

Waist circumference and waist/hip ratio in relation to all-cause mortality, cancer and sleep apnea

Relationship Between Obstructive Sleep Apnea and Increased Thoracic Periaortic Adipose Tissue

Síndrome metabólico y sus componentes en pacientes con síndrome de apnea-hipoapnea del sueño

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