Overall and coronary heart disease mortality rates in relation to major risk factors in 325,348 men screened for the MRFIT

Kannel, William B.; Neaton, James D.; Wentworth, Deborah; Thomas, Howard; Stamler, Jeremiah; Hulley, Stephen B.; Kjelsberg, Marcus O.

doi:10.1016/0002-8703(86)90481-3

Cited by 452 publications

(171 citation statements)

References 17 publications

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“…It is known that serum lipid levels are strongly correlated with atherosclerosis (1)(2). Elevated levels of high-density lipoprotein cholesterol (HDL-C) provide a cardioprotective effect while a high level of low-density lipoprotein cholesterol (LDL-C) is atherogenic (2)(3)(4).…”

Section: Introductionmentioning

confidence: 99%

A new index (CHOLINDEX) in detecting coronary artery disease risk

Akpınar¹,

Bozkurt²,

Acartürk³

et al. 2013

AnadoluKardiyolDerg

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Objective: Coronary artery disease (CAD) risk increases with the elevation of low-density lipoprotein cholesterol (LDL-C), triglyceride (TG) and low level high-density lipoprotein cholesterol (HDL-C) levels. However, the magnitude at which CAD risk increases with every lipid parameter is controversial. We developed a new index called CHOLINDEX, in order to evaluate CAD risk, and investigated its reliability. Methods: Three hundred and seven patients (190 males and 117 females, aged between 26-80 years, mean 53.6±10.2 years) who underwent diagnostic coronary angiography were included in the study. Risk factors and lipid profiles of all patients were noted. CHOLINDEX was calculated by using a formula as follows: CHOLINDEX=LDL-C-HDL-C (TG < 400 mg/dL), LDL-C-HDL-C + 1/5 of TG (TG ≥ 400mg/dL). Results: Of the 307 patients, 180 had CAD. We found that age, male gender, hypertension, diabetes mellitus, smoking and CHOLINDEX were independent predictors of CAD. The logistic regression analysis showed that the CHOLINDEX had a much more significant relation with CAD (odds ratio=1.011, 95% CI=1.003-1.019) compared with other lipid parameters. Conclusion: CHOLINDEX is a simple index which can be used reliably in prediction of CAD like other lipid parameters in daily clinical practice. (Anadolu Kardiyol Derg 2013; 13: 315-9)

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Section: Introductionmentioning

confidence: 99%

A new index (CHOLINDEX) in detecting coronary artery disease risk

Akpınar¹,

Bozkurt²,

Acartürk³

et al. 2013

AnadoluKardiyolDerg

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mentioning

confidence: 99%

“…1,2 Both conditions are common in the general population and are frequently associated, thereby steeply increasing the risk of overall and coronary heart disease mortality. 1 Identification of patients at high risk by detection of preclinical disease, when target-organ damage may be in an early and potentially reversible stage, has been emphasized in recent years as a key step in the prevention of major clinical events. 3 Previous reports have shown a close relationship between carotid atherosclerosis and both cerebrovascular and coronary artery disease, 4,5 suggesting that carotid artery evaluation may help to identify high-risk subjects.…”

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confidence: 99%

Carotid Intimal-Medial Thickness and Stiffness Are Not Affected by Hypercholesterolemia in Uncomplicated Essential Hypertension

Saba

Roman

Longhini

et al. 1999

ATVB

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Abstract-The combined effects of hypertension and hypercholesterolemia on carotid anatomy and stiffness were studied in 62 normotensives, 141 uncomplicated essential hypertensives with a total cholesterol level Ͻ240 mg/dL, and 60 essential hypertensives with a total cholesterol level Ն240 mg/dL. Carotid ultrasonography was performed to evaluate intimal-medial thickness (IMT), relative wall thickness, and the presence of plaque. Carotid pressure waveforms were recorded by applanation tonometry to measure carotid stiffness (␤) and pressure wave reflection (ie, augmentation index). After adjusting for age, body mass index, and smoking habit by analysis of covariance, no significant differences were found between normocholesterolemic hypertensives and hypercholesterolemic hypertensives in terms of IMT (0

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“…Weight¯uctuations are known to affect both serum lipids (Kannel et al, 1986) and hemostatic variables (Andersen et al, 1988). A major task in the planning of controlled dietary intervention studies is therefore to predict the participants individual energy requirements in order to maintain stable body weights during the study period (Mertz et al, 1991;Sandstro Èm, 1993;de Vries et al, 1994).…”

Section: Introductionmentioning

confidence: 99%

An assessment of the use of simple methods to predict individual energy intakes for intervention studies

1998

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Objective:To investigate to what extent individual energy intakes can be predicted by rapid easily available low-cost estimation methods. Design: Data were obtained from a controlled dietary intervention study period of nine weeks in which the subjects should be weight stable. Subjects: Thirty-one male students in domestic and kitchen management aged 29 AE 6 y. Methods: (1) energy intake calculated from a quantitative food frequency questionnaire (FFQEI); (2) energy expenditure derived from estimates of basal metabolic rate (BMR) (FAOaWHOaUNU, 1985) based on weight, gender, age and low (1.556BMR), medium (1.786BMR) or high (2.106BMR) level of activity. Level of activity was determined by questions concerning habitual activities lasting more than 20 min (WHOEE); (3) energy expenditure derived from individual recording in a specially prepared activity diary (ADEE). During the intervention, the subjects were to be fed test diets which should provide them with enough energy to keep them weight stable. The energy levels were established after taking both the FFQEIs, WHOEEs and ADEEs into consideration, and 10 MJ, 13 MJ, 15 MJ and 17 MJ per day were chosen because these levels were estimated to closely match the energy requirements of most of the subjects. The levels of energy were changed during the intervention period if the weight of the subjects¯uctuated. The served level of energy at the last day of the intervention was denoted the weight maintenance energy intake (WMEI). WMEI was compared to FFQEI, WHOEE and ADEE in order to evaluate if one estimation method predicted WMEI better than the two others. Results: None of the three methods provided accurate estimates of WMEI of 13.3 AE 1.8 MJ. However, WHOEE, gave the best estimate as demonstrated by the limits of agreement: 78.7 MJ to 8.9 MJ for FFQEI, 75.4 MJ to 3.9 MJ for WHOEE and 77.2 MJ to 5.2 MJ for ADEE. The coef®cients of correlation between the differences and the means of WMEI and FFQEI, WHOEE and ADEE were 70.8 (P 0.001), 0.1 (P 0.6, NS) and 70.5 (P 0.01), respectively. The coef®cients of variation were 34.6% for FFQEI, 11.3% for WHOEE, and 21.0% for ADEE. Conclusions: Although not precise, WHOEE showed the best agreement with the WMEI. These results demonstrate that a rapid and simple low-cost method predicted WMEI closely enough to avoid major weight uctuations among these men during the intervention period.

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Overall and coronary heart disease mortality rates in relation to major risk factors in 325,348 men screened for the MRFIT

Cited by 452 publications

References 17 publications

A new index (CHOLINDEX) in detecting coronary artery disease risk

A new index (CHOLINDEX) in detecting coronary artery disease risk

Carotid Intimal-Medial Thickness and Stiffness Are Not Affected by Hypercholesterolemia in Uncomplicated Essential Hypertension

An assessment of the use of simple methods to predict individual energy intakes for intervention studies

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