Non-fasting Lipid Profile Research Articles

Non-fasting lipid profile determination in presumably healthy children: Impact on the assessment of lipid abnormalities.

ObjectiveDespite the common use of non-fasting measurements for lipid profile in children it remains unclear as to the extent non-fasting conditions have on laboratory results of lipids measurements. We aimed to assess the impact of non-fasting lipid profile on the occurrence of dyslipidemia in children.Materials and methodsBasic lipid profile including: total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG), as well as small, dense-LDL-C (sd-LDL-C), apolipoprotein AI (ApoAI), apolipoprotein B (ApoB) and lipoprotein(a) [Lp(a)], were measured in 289 presumably healthy children aged 9–11 in both fasting and non-fasting condition. The clinical impact of non-fasting lipid profile was evaluated individually for each child with estimation of false positive (FP) and false negative (FN) results.ResultsThe highest percentage of FP results in non-fasting condition was observed for TG (42.3%) being significantly higher when compared to FN results (p = 0.003). In contrast, the highest percentage of FN results in a non-fasting state were shown for LDL-C (14.3%), but the difference was statistically insignificant when compared to FP results. When comparing fasting and non-fasting lipid profile a number of significant differences was shown for: TG (p<0.001), HDL-C (p = 0.002) LDL-C (p<0.001) and ApoAI (p<0.001), respectively. The occurrence of dyslipidemia, recognized on the basis of non-fasting lipids was significantly higher (p = 0.010) when compared to fasting lipid profile.ConclusionsA higher occurrence of dyslipidemia, based on the measurement of non-fasting lipids in children, is suggestive of possible disorders in lipid metabolism. However, accurate identification of dyslipidemia by assessment of non-fasting lipids requires the establishment of appropriate cut-off values for children.

Clinical hotspots and controversies about dyslipidemia and cardiovascular diseases

Atherosclerotic cardiovascular disease (ASCVD) is a chronic and complex disease with multiple risk factors. Among these risk factors, only dyslipidemia is a pathogenicity risk factor. In this article, we will discuss the following hot spots and controversies about dyslipidemia and ASCVD. Are lipid parameters just risk factors or targets of therapy? Which of the cholesterol concentration and lipoprotein particle number has more clinical values? Should non-fasting or fasting lipid profiles be preferred? (Chin J Lab Med, 2018, 41: 415-419) Key words: Coronary disease; Dyslipidemias; Lipids; Risk factors

Fasting and Non-Fasting Lipid Profile among Health Care Workers at Teaching Hospital Batticaloa SriLanka

Requesting patients to come with long fasting state (12 - 14 hours) for lipid profile measurements are a major inconvenience. However, most blood tests, even glycemic management, can be done in a non-fasting state, for example by requesting an HbA1C. It is understandable that when we order lipid profile test, laboratories are very rigid on fasting (12 - 14 h) or refuse to do the test if fasting is not enough. To answer these delusions, we conducted a cross-sectional study among the health care workers at Teaching Hospital Batticaloa, SriLanka, after an overnight fast (12 - 14 hours) blood taken for lipid profile. Following weeks, we collected blood from the same healthcare workers, after breakfast (2 - 4 hours after meal). In this study, measurements of fasting lipid profile, including high-density lipoproteins (HDL), low-density lipoproteins (LDL), total cholesterol (TC), triglyceride (TG) and non-HDL significantly (p < 0.05) differ from non-fasting lipid profile measurement. The mean difference in lipid profile in fasting versus non-fasting among healthcare workers was 34.13 mg/dl for TG, -5.65 mg/dl for total TC, -1.94 mg/dl for HDL-cholesterol, 3.71 mg/dl for non-HDL and 12.3 mg/dl for LDL-cholesterol. This momentous change of different meanings does not play any significant role in cardiovascular risk assessment. However, a patient with a family history of the premature atherosclerotic cardiovascular disease (ASCVD), or familial hyperlipidemia, screening and follow-up should preferably be performed with fasting.

Comparison of fasting and non-fasting lipid profiles in a large cohort of patients presenting at a community hospital

ObjectiveTo compare the fasting and non-fasting lipid profile including ApoB in a cohort of patients from a community setting. Our purpose was to determine the proportion of results that could be explained by the known biological variation in the fasting state and to examine the additional impact of non-fasting on these same lipid parameters. Methods1093 adult outpatients with fasting lipid requests were recruited from February to September 2016 at the blood collection sites of the Moncton Hospital. Participants were asked to come back in the next 3–4days after having eaten a regular breakfast to have their blood drawn for a non-fasting lipid profile. Results91.6% of patients in this study had a change in total cholesterol that fell within the biological variation expected for this parameter. Similar results were seen for HDL-C (94.3%) non-HDL-C (88.8%) and ApoB (93.0%). A smaller number of patients fell within the biological variation expected for TG (78.8%) and LDL-C (74.6%). An average TG increase of 0.3mmol/L was observed in fed patients no matter the level of fasting TG. A gradual widening in the range of change in TG concentration was observed as fasting TG increased. Similar results were seen in diabetic patients. ConclusionOutside of LDL-C and TG, little changes were seen in lipid parameters in the postprandial state. A large part of these changes could be explained by the biological variation. We observed a gradual widening in the range of increase in TG for patients with higher fasting TG. Non-HDL-C and ApoB should be the treatment target of choice for patients in the non-fasting state.

P636Risk of misclassification with a non-fasting lipid profile in secondary cardiovascular prevention

P636Risk of misclassification with a non-fasting lipid profile in secondary cardiovascular prevention

Risk of misclassification with a non-fasting lipid profile in secondary cardiovascular prevention

AimsRoutinely fasting is not necessary for measuring the lipid profile according to the latest European consensus. However, LDL-C tends to be lower in the non-fasting state with risk of misclassification. The extent of misclassification in secondary cardiovascular prevention with a non-fasting lipid profile was investigated. Methods and results329 patients on lipid lowering therapy for secondary cardiovascular prevention measured a fasting and non-fasting lipid profile. Cut-off values for LDL-C, non-HDL-C and apolipoprotein B were set at <1.8mmol/l, <2.6mmol/l and <0.8g/l, respectively. Study outcomes were net misclassification with non-fasting LDL-C (calculated using the Friedewald formula), direct LDL-C, non-HDL-C and apolipoprotein B. Net misclassification <10% was considered clinically irrelevant. Mean age was 68.3±8.5years and the majority were men (79%). Non-fasting measurements resulted in lower LDL-C (−0.2±0.4mmol/l, P<0.001), direct LDL-C (−0.1±0.2mmol/l, P=0.001), non-HDL-C (−0.1±0.4mmol/l, P=0.004) and apolipoprotein B (−0.02±0.10g/l, P=0.004). 36.0% of the patients reached a fasting LDL-C target of <1.8mmol/l with a significant net misclassification of 10.7% (95% CI 6.4–15.0%) in the non-fasting state. In the non-fasting state net misclassification with direct LDL-C was 5.7% (95% CI 2.1–9.2%), 4.0% (95% CI 1.0–7.4%) with non-HDL-C and 4.1% (95% CI 1.1–9.1%) with apolipoprotein B. ConclusionUse of non-fasting LDL-C as treatment target in secondary cardiovascular prevention resulted in significant misclassification with subsequent risk of undertreatment, whereas non-fasting direct LDL-C, non-HDL-C and apolipoprotein B are reliable parameters.

Comparison between fasting and nonfasting lipid profile in patients receiving treatment with statin therapy

Objectives The aim of this study was to assess the effect of ordinary meal on the lipid profile of patients receiving statin therapy. Background The lipid profile is an essential investigation for the patient who comes to cardiology outpatient clinic. Numerous guidelines recommend sampling in the fasting state for cardiovascular risk assessment. Fasting for 12–14 h is not only cumbersome and unpleasant for patients, but may result in limited compliance to disease monitoring and treatment. Patients and methods This study was performed on 100 patients with dyslipidemia receiving statin therapy whose doses had not changed for 2 or more months (group I), and 100 patients with dyslipidemia not receiving statin therapy (group II). Lipid profile was determined for fasting and postprandial statuses. Results The lipid profile parameters in both groups in fasting and postprandial statuses were compared. In group I, the mean fasting serum triglyceride level was 176.21 mg/dl and mean postprandial serum triglyceride level was 213.49 mg/dl (P = 0.0001); the mean fasting low-density lipoprotein (LDL) level was 161.19 mg/dl and mean postprandial LDL was 159.25 mg/dl (P = 0.184). In group II, the mean fasting serum triglyceride level was 231.06 mg/dl and mean postprandial triglyceride level was 284.60 mg/dl (P = 0.005); the mean fasting LDL was 185.18 mg/dl and mean postprandial LDL was 181.32 mg/dl (P = 0.871). Conclusion Finally, from this study we found that there is no significant clinical difference between fasting and nonfasting levels of total cholesterol, high-density lipoprotein, and LDL. Thus, we can use the nonfasting tests to follow-up the dyslipidemic patients.

Noncommunicable disease in rural India: Are we seriously underestimating the risk? The Nallampatti noncommunicable disease study.

Aim:To assess the prevalence of noncommunicable diseases in a true rural farming population in South India and compare the data with the landmark contemporary Indian Council of Medical Research-India Diabetes (ICMR-INDIAB) study.Methods:Local Ethics Committee approval and informed consent was obtained from all participants. Inclusion criteria were participants, aged ≥20 and ≤85 years, from Nallampatti, a classical farming village from Tamil Nadu state, India. All participants were administered a detailed questionnaire, had anthropometric measurements including height, weight, and waist circumference. Bloods were drawn for random blood glucose, glycated hemoglobin (HbA1c), nonfasting lipid profile, Cystatin C, uric acid, and hemoglobin. All participants had carotid intima-media thickness (CIMT) done by high-resolution B-mode carotid ultrasound.Results:More than 50% of the population had either diabetes or prediabetes based on HbA1c. Nearly, 40% of the population had hypertension with suboptimal control in those with known hypertension. Nearly, a third of the population had dyslipidemia, elevated cystatin C levels, and abnormal CIMT. The burden was higher than the comparable ICMR-INDIAB study in rural Tamil Nadu.Conclusion:One-third to one-half of this rural farming population is at risk of cardiovascular disease, with poor control of preexisting cardiovascular risk factors. Current Indian data may underestimate the risk in different ethnic populations and regions of India. Long-term follow-up of this cohort for the incident cardiovascular disease will shed light on the true cardiovascular risk in a typical South Indian rural farming population.

Cystatin Levels in a Rural South Indian Population

Abstract Aim Our aim was to study the potential role of Cystatin C as a marker for renal function and cardiovascular disease in a rural South Indian population. Methods Local Ethics Committee approval and informed consent was obtained from all participants. Inclusion criterion was that participants had to be ≥20 and ≤85 years of age. All participants were administered a detailed questionnaire, and had their anthropometric measurements taken including height, weight, and waist circumference. Blood was drawn for determining random glucose, glycosylated hemoglobin, nonfasting lipid profile, Cystatin C, uric acid, and hemoglobin. Urine samples were collected for urine protein creatinine ratio. All participants had their carotid intima thickness determined by high-resolution B-mode carotid ultrasound. Results A total of 865 participants were screened in this study. Cystatin C value of >1 mg/l and creatinine of >1.1 mg/dl in females and 1.3 mg/dl in males were defined as “elevated levels.” Nearly a third of this population had elevated Cystatin C levels. Higher levels of Cystatin C, even within the normal creatinine range, was associated with cardiovascular risk markers like age, blood pressures, uric acid, and carotid intima thickness. Conclusions Cystatin C in a rural South Indian population correlates well with creatinine and cardiovascular markers. Long-term follow-up of this cohort stratified on the basis of Cystatin C will be extremely helpful in augmenting the utility of Cystatin C as an atherosclerotic risk marker in an Indian population.

Accuracy of Non-Fasting Lipid Profile for the Assessment of Lipoprotein Coronary Risk.

To determine the diagnostic accuracy of non-fasting lipid profile in the diagnosis of hyperlipidemia, taking fasting lipid profile as gold standard, in adult population. Cross-sectional validation study. Department of Chemical Pathology and Endocrinology, Armed Forces Institute of Pathology, Rawalpindi, from July to December 2014. One hundred seventy-five adult patients coming for fasting lipid profile were included; their non-fasting samples were taken on the next day. Patients on anti-cholesterol treatment and indoor patients were excluded. Total cholesterol (TC), high density lipoprotein-cholestrol (HDL-C), and triglycerides were measured by direct enzymatic colorimetric method by Modular p-800®. Low density lipoprotein-cholesterol (LDL-C) was calculated by Friedewald's formula, but when triglyceride was greater than 4.5 mmol/l, then LDL-C was measured directly by homogenous enzymatic colorimetric method. Non-HDL-C was calculated by simple equation, i.e. TC-HDL-C. Non-fasting lipid profile had 93% specificity , 51% sensitivity, 94% positive predictive value and 49% negative predictive value; and 65% accuracy with 7.28 positive likelihood ratio and 0.52 negative likelihood ratio. Non-fasting TC and non-HDL-C were significantly higher than fasting TC and non-HDL-C by mean difference of 0.2 mmol/l each with p=0.001 and p=0.004, respectively. Fasting and non fasting HDL-C are comparable to each other with mean difference of 0.01 mmol/l (p=0.745). Receiver operating curve (ROC) of non-fasting non-HDL-C showed 0.804 (95%CI (0.738-0.870), (p=0.000) area under the curve (AUC) indicating that it was a significant test for ruling out hyperlipidemia. Bland-Altmann plot showed a significant difference between non-fasting, non-HDL-C and fasting LDL-C and non-fasting, non-HDL-C -0.087540 with bias -0.00109; therefore, these cannot be alternative to each other. Diagnostic accuracy of non-fasting lipid profile was found significantly higher than fasting lipid profile (p=0.004) for the assessment of lipoprotein coronary risk on the basis of non-HDL-C, which seemed to be significant test for ruling out hyperlipidemia.

Glycosylated hemoglobin-defined prediabetes and cardiovascular risk markers in rural India: the Nallampatti noncommunicable disease study

The aim of this study is to assess the associations between glycosylated hemoglobin in the prediabetes range and cardiovascular risk markers in a rural South Indian population. Local Ethics Committee approval and informed consent was obtained from all participants. Inclusion criteria were participants, aged ≥20 and ≤85 years, from Nallampatti, a classical farming village from Tamil Nadu State, India. Those with known history of diabetes were excluded from this analysis. All participants were administered a detailed questionnaire, had anthropometric measurements including height, weight, and waist circumference. Bloods were drawn for random blood glucose, glycosylated hemoglobin, nonfasting lipid profile, cystatin C, uric acid, and hemoglobin. All participants had carotid intima thickness done by high-resolution B-mode carotid ultrasound. Progressive hyperglycemia across the glucose tolerance continuum based on glycosylated hemoglobin levels in a rural South Indian population seems to be associated with worsening cardiovascular risk markers. A cut-off value of ≥6% (42 mmol/mol) seems to herald a much more significant increase in such markers. Long-term follow-up of this cohort for incident cardiovascular disease will help to substantiate the associations between glycosylated hemoglobin levels within the prediabetes range and cardiovascular disease in an Indian population. Evidence-based race-specific criteria for diagnosis of prediabetes and diabetes are the need of the hour for risk stratification and appropriate management.

Frontiers in lipid research: mechanisms, diet, and novel lipids

Download the Issue @ a Glance podcast Subscribe to the EHJ Podcast ![Graphic][1] The measurement of lipid profiles is crucial in primary and secondary prevention, and is recommended by the most recent European Society of Cardiology (ESC) Prevention Guidelines.1 There is, however, uncertainty on how to do this properly. The European Atherosclerosis Society and the European Federation of Clinical Chemistry and Laboratory Medicine clarify this issue in their joint consensus statement ‘ Fasting is not routinely required for determination of a lipid profile: clinical and laboratory implications including flagging at desirable concentration cut-points .’ The authors critically evaluate the clinical implications of the use of non-fasting rather than fasting lipid profiles, and provide guidance for laboratory reporting of abnormal non-fasting or fasting lipid profiles.2 Extensive observational data, in which random non-fasting lipid profiles were compared with those determined under fasting conditions, indicate that the maximal mean changes at 1–6 h after habitual meals are not clinically significant and averaged 0.3 mmol/L or 26 mg/dL for triglycerides, 0.2 mmol/L or 8 mg/dL for total cholesterol, 0.2 mmol/L or 8 mg/dL for LDL-cholesterol, 0.2 mmol/L or 8 mg/dL for calculated remnant cholesterol, and 0.2 mmol/L or 8 mg/dL for calculated non-HDL cholesterol. Concentrations of HDL-cholesterol, apolipoprotein A1, apolipoprotein B, and lipoprotein(a) were not affected by fasting or non-fasting status, respectively. In addition, non-fasting and fasting concentrations vary similarly over time and are comparable in their ability to predict cardiovascular disease. To improve patient compliance with lipid testing, the authors therefore recommend routine use of non-fasting lipid profiles, while fasting sampling may be considered when non-fasting triglycerides are higher than 5 mmol/L or 440 mg/dL. For non-fasting samples, laboratory reports should flag abnormal concentrations of triglycerides ≥2 mmol/L or 175 mg/dL, total cholesterol ≥5 mmol/L or 195 mg/dL, LDL-cholesterol ≥3 mmol/L or … [1]: /embed/inline-graphic-1.gif

A Study to Establish the Validity of Non-Fasting Lipid Profile against Fasting Lipid Profile for Making Treatment Decision in Diabetes Mellitus 2012-2014

As incidence of diabetes is continuously increasing in india and diabetic dyslipidemia is a leading cause of atherosclerotic disease process like coronary artery disease. If we diagnosed and treat dyslipidemia in early stage, we can decrese the risk of atherosclerotic diseases. But current guidelines recommend measurement of fasting lipid profile and majority of the patients present in the hospital are in nonfasting state so their lipid profile is deferred due to their nonfasting status which impedes their treatment. However, recent studies suggest nonfasting lipid profile may better or similarly predict cardiovascular disease events than fasting levels. So, if validate for treatment of diabetic dyslipidemia then measurement of nonfasting lipid profile may have many practical advantage for clinical practice. So a prospective study was carried out in 100 diabetic patients in 25-65 years age group including both sexes irrespective of community or background which were present in inpatient department of medicine, Mata Chanan Devi hospital, New Delhi, during the time period of 2012-2014. It is a 210 bedded, tertiary care hospital in west Delhi, where the patients travel from all north India. The patients with Coronary event (myocardial infarction, unstable angina) and procedures (coronary artery bypass, coronary angioplasty) in previous 4 weeks, ischemic stroke in prior 3 months, chronic alcoholic patients, patients having pancreatitis, hypothyroidism, pregnancy and patients on lipid lowering drugs in previous 3 months, were not included in the study. After taking written informed consent lipid profile was done from each subject at the time of admission and next day morning (after 8-12 hr fasting). Self reported time since last meal was also noted at the time of admission. LDL was measured by direct method. Non HDL cholesterol was calculated by total cholesterol – HDL cholesterol. SPSS (statistical package for social sciences) software version 16.0 was used for statistical analysis. Wilcoxon Signed Ranks test done to compare the total mean fasting and nonfasting lipid profile. We divided the study subjects according to self reported time since last meal. Like 0-1 hour include patient taking meal before < 1 hour and 1-2 hour include <2 hour post meal and so on other groups. Comparison of fasting and non fasting lipid profile was done by Wilcoxon

Fasting is not routinely required for determination of a lipid profile: clinical and laboratory implications including flagging at desirable concentration cut-points—a joint consensus statement from the European Atherosclerosis Society and European Federation of Clinical Chemistry and Laboratory Medicine

AimsTo critically evaluate the clinical implications of the use of non-fasting rather than fasting lipid profiles and to provide guidance for the laboratory reporting of abnormal non-fasting or fasting lipid profiles.Methods and resultsExtensive observational data, in which random non-fasting lipid profiles have been compared with those determined under fasting conditions, indicate that the maximal mean changes at 1–6 h after habitual meals are not clinically significant [+0.3 mmol/L (26 mg/dL) for triglycerides; −0.2 mmol/L (8 mg/dL) for total cholesterol; −0.2 mmol/L (8 mg/dL) for LDL cholesterol; +0.2 mmol/L (8 mg/dL) for calculated remnant cholesterol; −0.2 mmol/L (8 mg/dL) for calculated non-HDL cholesterol]; concentrations of HDL cholesterol, apolipoprotein A1, apolipoprotein B, and lipoprotein(a) are not affected by fasting/non-fasting status. In addition, non-fasting and fasting concentrations vary similarly over time and are comparable in the prediction of cardiovascular disease. To improve patient compliance with lipid testing, we therefore recommend the routine use of non-fasting lipid profiles, while fasting sampling may be considered when non-fasting triglycerides >5 mmol/L (440 mg/dL). For non-fasting samples, laboratory reports should flag abnormal concentrations as triglycerides ≥2 mmol/L (175 mg/dL), total cholesterol ≥5 mmol/L (190 mg/dL), LDL cholesterol ≥3 mmol/L (115 mg/dL), calculated remnant cholesterol ≥0.9 mmol/L (35 mg/dL), calculated non-HDL cholesterol ≥3.9 mmol/L (150 mg/dL), HDL cholesterol ≤1 mmol/L (40 mg/dL), apolipoprotein A1 ≤1.25 g/L (125 mg/dL), apolipoprotein B ≥1.0 g/L (100 mg/dL), and lipoprotein(a) ≥50 mg/dL (80th percentile); for fasting samples, abnormal concentrations correspond to triglycerides ≥1.7 mmol/L (150 mg/dL). Life-threatening concentrations require separate referral when triglycerides >10 mmol/L (880 mg/dL) for the risk of pancreatitis, LDL cholesterol >13 mmol/L (500 mg/dL) for homozygous familial hypercholesterolaemia, LDL cholesterol >5 mmol/L (190 mg/dL) for heterozygous familial hypercholesterolaemia, and lipoprotein(a) >150 mg/dL (99th percentile) for very high cardiovascular risk.ConclusionWe recommend that non-fasting blood samples be routinely used for the assessment of plasma lipid profiles. Laboratory reports should flag abnormal values on the basis of desirable concentration cut-points. Non-fasting and fasting measurements should be complementary but not mutually exclusive.

Higher BNP levels within physiological range correlate with beneficial nonfasting lipid profiles in the elderly: a cross-sectional study.

BackgroundEmerging studies indicate that B-type natriuretic peptide (BNP), a well-known biomarker for heart failure, also plays pivotal roles in metabolic control. Circulating BNP levels progressively increase as ages grow older. However, the association between BNP levels and lipid metabolism in the elderly remains unknown.MethodsA total of 680 eligible volunteers (male/female: 334/346) aged between 60 and 80 years old without overt heart failure (BNP <100 pg/ml) were enrolled. Random nonfasting venous samples were obtained for biochemical analysis. The subjects were stratified based on BNP quartiles: BNP Q1 (range: 2.2–9.0 pg/ml), Q2 (9.1–20.4 pg/ml), Q3 (20.5–44.4 pg/ml) and Q4 (44.6–99.7 pg/ml). Difference of metabolic parameters was compared among the subjects grouped by BNP quartiles. Univariate correlation and multiple linear regression were performed to analyze the association between BNP levels and metabolic parameters. The odds ratios (OR) and 95 % confidence intervals (CI) for dyslipidemia in subjects within BNP Q1-3 relative to subjects within BNP Q4 were calculated.ResultsCirculating BNP levels positively correlated with age, while negatively correlated with body mass index (BMI), eGFR and non-HDL. Subjects with lower BNP quartiles had significantly elevated prevalence of dyslipidemia, including hypertriglyceridemia, hyper-LDL-emia and hypercholesterolemia. The OR of hypertriglyceridemia and hypercholesterolemia for subjects within BNP Q1-2 significantly increased relative to BNP Q4.ConclusionsThe elderly people with higher BNP levels have significantly reduced risks for nonfasting dyslipidemia. Verification of the cause-effect relationship between BNP and dyslipidemia may bring therapeutic implications.

Atherogenic Risk Assessment among Persons Living in Rural Uganda

Background. Hypertension and dyslipidemia are independent risk factors for coronary heart disease and commonly coexist. Cardiovascular risk can be reliably predicted using lipid ratios such as the atherogenic index, a useful prognostic parameter for guiding timely interventions. Objective. We assessed the cardiovascular risk profile based on the atherogenic index of residents within a rural Ugandan cohort. Methods. In 2011, a population based survey was conducted among 7507 participants. Sociodemographic characteristics, physical measurements (blood pressure, weight, height, and waist and hip circumference), and blood sampling for nonfasting lipid profile were collected for each participant. Atherogenic risk profile, defined as logarithm base ten of (triglyceride divided by high density lipoprotein cholesterol), was categorised as low risk (<0.1), intermediate risk (0.1–0.24), and high risk (>0.24). Results. Fifty-five percent of participants were female and the mean age was 49.9 years (SD ± 20.2). Forty-two percent of participants had high and intermediate atherogenic risk. Persons with hypertension, untreated HIV infection, abnormal glycaemia, and obesity and living in less urbanised villages were more at risk. Conclusion. A significant proportion of persons in this rural population are at risk of atherosclerosis. Key identified populations at risk should be considered for future intervention against cardiovascular related morbidity and mortality. The study however used parameters from unfasted samples that may have a bearing on observed results.

Non-Fasting Lipid Profiles and Cardiovascular Risk Assessment

Non-Fasting Lipid Profiles and Cardiovascular Risk Assessment

EVALUATING THE ASSOCIATIONS BETWEEN BUYING LUNCH AT SCHOOL, EATING AT RESTAURANTS, AND EATING TOGETHER AS A FAMILY AND CARDIOMETABOLIC RISK IN ADOLESCENTS

Purchasing lunch at school, eating at restaurants, and infrequently eating dinner as a family may be associated with increased cardiometabolic risk. We sought to evaluate these associations in adolescents undergoing universal school-based screening. We performed a cross-sectional study of grade 9 students over 4 school years (2009-2013) undergoing universal school-based screening through the Heart Niagara Inc. Healthy Heart Schools’ Program. Questionnaires assessed frequency of buying lunch at school, eating at restaurants, and eating dinner with at least one family member, amongst other behaviours. Height, weight, waist circumference, blood pressure, and non-fasting lipid profiles were measured. N=14,280 (52% male, 14.6+0.6 years old). Seventeen percent (2,289) were overweight (BMI 85-<95th percentile) and 14% (1,863) were obese (BMI >95th percentile). Nine percent (1,097) reported almost always/always buying lunch at school. Eating out at a restaurant at least twice per week was reported in 27% (3,327). Dinner with a family member <1 time per week was reported in 8% (963). Buying lunch more frequently at school was associated with a 0.029mmol/L (LCL 0.007, UCL 0.051) increase in non-HDL-C (Table). There were no clinically significant associations between frequency of eating at restaurants and BMI, lipid profile, or blood pressure (Table). Increased weekly frequency of eating dinner with at least 1 family member was significantly associated with a decreased BMI percentile, waist-to-height ratio, total cholesterol, non-HDL cholesterol and systolic BP Z-score (Table and Figure). Eating with at least one person 0-1 times per week had 1.5 (1.34-1.65, p<0.001) times increased odds of being in a higher frequency category of buying lunch at school compared with children who eating with a family member more than once per week. Children who reported almost always/always buying lunch at school had 1.84 (1.77-1.92, p<0.001) times increased odds of eating at restaurants more frequently and 7.3 (6.3-8.4, p<0.001) times increased odds of eating at restaurants >3 times per week compared with children who reported sometimes or never buying lunch at school. Eating dinner as a family more frequently was associated with decreased cardiometabolic risk factors. There were no clinically significant associations between buying lunch at school or eating at restaurants and cardiometabolic risk. There were significant associations between infrequently eating together as a family and buying lunch at school and buying lunch at school and more frequently eating at restaurants. These associations may help identify potentially modifiable risk factors regarding adolescent eating behaviours and cardiometabolic risk.View Large Image Figure ViewerDownload (PPT)

42 Ireland versus Poland away, what’s the score? risk prediction using score charts for immigrants from high risk european countries in an Irish setting

Introduction Deaths due to cardiovascular disease (CVD) number 4 million annually in Europe with significantly higher mortality rates seen in Central and Eastern Europe. Large numbers of citizens from these regions migrated to Ireland in the last decade with those of Polish nationality now comprising the largest minority ethnic group in the country. The health status of these immigrants and the potential impact of migration on national CVD risk levels has not been studied. There is no guidance from the European Society of Cardiology (ESC) to date as to which SCORE chart to use in the case of an individual from a high risk country who has immigrated to a low risk nation. Methods Immigrant adults over 18 years and from any of the high risk countries of Europe (according to the SCORE risk estimation tool) were recruited via convenience sampling in Limerick city. Screening was offered in occupational and community settings; a non-fasting lipid profile, blood pressure (BP) measurements, subjective questioning regarding physical activity levels, alcohol consumption and smoking status. The SCORE risk tool and relative risk categorisation were used to calculate CVD risk. Results 81 participants (mean age 41 years) took part in this study. The majority were Polish (n = 72, 89%) and 54% (n = 44) were male. Subjects were divided into two groups for data analysis; those aged ≥40 years (n = 38, 46.9%, mean age 52 years) and younger subjects (n = 43, 53.1%, mean age 30 years). Very high levels of hypertension (55%) and hypercholesterolaemia (61%) were observed in those aged ≥40 years and 42% (n = 16) of the older subjects displayed a high or very high ten-year risk of death due to CVD. SCORE was strongly correlated with age (r = 0.70, p Conclusions This study demonstrates that CVD risks inherent in a high risk population are preserved following immigration into a low risk area. As no current guidance exists from the ESC, the authors feel this cohort of people should undergo targeted risk factor modification in line with recommendations for high risk individuals. We have highlighted a deficiency in the SCORE risk charts, and feel further work is required to accurately assess the disease burden in this unique patient group.

Influence of a Regular, Standardized Meal on Lipid Profile of People with Diabetes

Aims: The need to fast before lipid screening has been questioned in the past years. The aim of this study was to determine the influence of a light meal on the lipid profile in people with diabetes. Methods: 115 participants with type 2 diabetes were recruited between April/2013-August/2014 from our Outpatient Diabetes Education Clinic. Clinical and analytical evaluation took place in 2 moments (8-hour fasting=t0; 2h after a light standardized meal=t1), with measurements of total cholesterol, LDL-cholesterol, HDL-cholesterol and triglycerides. Results: Triglycerides concentration increased between the 2 moments (median difference t1-t0=0.07 mmol/L, p=0.002) but the total cholesterol, LDL-cholesterol, HDL-cholesterol and non HDL- cholesterol did not change significantly. Performing an analysis according to the LDL-cholesterol therapeutic goals proposed by Adult Treatment Panel III, we found an agreement between fasting and postprandial assessments of 91.1% for the goal of 2.6 mmol/L (102/112), and of 97.3% for the goal of 1.8 mmol/L (109/112). The same analysis was performed for the secondary goal, non HDL-cholesterol. Conclusion: The data presented suggest that the nonfasting lipid profile can be an alternative to the fasting lipid profile in selected patients. Larger studies are needed to confirm these results and demonstrate an association of nonfasting lipemia and cardiovascular risk in individuals with diabetes.