Modeling the risk factors for dyslipidemia and blood lipid indices: Ravansar cohort study

Dietary intake of total, animal, and vegetable protein and cardiometabolic risk factors in patients with type 2 diabetes: using iso-energetic substitution models

Objectives

The present study aimed to examine the effects of dietary intake of total, animal-based, and vegetable-based protein on cardiometabolic risk factors in diabetic patients, using iso-energetic substitution models.

Methods

This cross-sectional study was a part of the Ravansar Non-Communicable Disease (RaNCD) cohort study with 8,894 subjects. Univariate and multivariate logistic regression models were used to determine the associations between total, animal, and vegetable protein intake (per 5% energy) and cardiometabolic risk factors. All analyses were carried out at a 95% confidence level using STATA software version 14.2.

Results

In diabetic patients, higher intake of total protein increased the risk of hypertension by 2.48 times compared to the reference group (p = 0.03). Besides, the association between the consumption of one unit of energy (5% energy) from protein at the expense of one unit of energy from fat and the risk of cardio-metabolic risk factors, showed an increase in dyslipidemia and CVDs by 65 and 48%, respectively. The substitution for carbohydrates also causes a reduction in obesity and abdominal obesity by 28 and 53%, respectively.

Conclusion

In diabetic and non-diabetic patients, different associations were observed following the substitution of protein. In diabetic patients, the substitution protein for fat increased the risk of dyslipidemia and CVDs and carbohydrate replacement increased the risk of dyslipidemia. The highest vs. the lowest intake of animal protein decreased the risk of obesity and abdominal obesity, whereas regarding plant protein a direct link was found with dyslipidemia. However, prospective studies are needed to clarify the cause-and-effect links.

Determinative sleep traits associated with dyslipidemia in obstructive sleep apnea patients

BACKGROUND

Obstructive sleep apnea (OSA) is recognized to increase the risk of dyslipidemia; however, the specific sleep traits in OSA that influence dyslipidemia are poorly understood. This study sought to determine which sleep traits are independently associated with dyslipidemia and serum lipid profiles in patients with OSA.

METHODS

In this cohort study, 5239 participants were included from the Sleep Heart Health Study. Further, OSA was diagnosed via polysomnography with an AHI ≥ 5 events/h. Sleep traits were assessed using polysomnographic data and questionnaires. Then, logistic regression was used to identify sleep traits that predict dyslipidemia in OSA patients. Non-linear associations between sleep traits and dyslipidemia were evaluated using restricted cubic splines. The potential mediating effect of body mass index (BMI) was also calculated. Later, linear regression analysis identified sleep traits that were independently linked to lipid levels.

RESULTS

After adjusting for confounding factors, logistic regression identified sleep latency (OR: 1.005, 95% CI: 1.002-1.009, P = 0.001), rapid eye movement (REM) stage (OR: 0.987, 95% CI: 0.977-0.998, P = 0.022), REM latency (OR: 1.001, 95% CI: 1.000-1.002, P = 0.027), mean oxygen saturation (meanSpO2) (OR: 0.961, 95% CI: 0.926-0.996, P = 0.031), percentage of time with oxygen saturation below 95% (T95) (OR: 1.003, 95% CI: 1.001-1.005, P = 0.005), and time to fall asleep (OR: 1.004, 95% CI: 1.000-1.007, P = 0.042) as variables independently associated with dyslipidemia. No significant non-linear associations were found (all P >0.05). BMI mediated the association between REM stage, meanSpO2, T95, and dyslipidemia risk. Linear regression analysis identified T95 as a consistent independent determinant of all lipid parameters. Additionally, the meanSpO2 and sleep latency were significant independent determinants of most lipid parameters.

CONCLUSIONS

Sleep latency, sleep architecture, and nocturnal hypoxemia are key factors in dyslipidemia among patients with OSA. These insights suggest potential biomarkers and targeted interventions for the management of lipid-related complications of OSA.

Diagnostic prediction model for screening of elevated low-density and non-high-density lipoproteins in young Thai adults between 20 and 40 years of age

BACKGROUND

Low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C) levels are paramount in atherosclerotic cardiovascular disease risk management. However, 94.4% of Thai young adult are unaware of their condition. A diagnostic prediction model may assist in screening and alleviating underdiagnosis.

OBJECTIVES

Development and internal validation of diagnostic prediction models on elevated LDL-C (≥160 mg/dL) and non-HDL-C (≥160 mg/dL).

METHODS

Retrospective, single-centre, tertiary-care hospital annual health examination data from 29 March 2018 to 30 August 2023 was analysed. Two models with 11 predictors from anthropometry and bioimpedance are fitted with multivariable binary logistic regression predicting elevated LDL-C and non-HDL-C. Predictor selection used the backward stepwise elimination. Four performance metrics were quantified: discrimination using area under the receiver-operating characteristic curve (AuROC); calibration by calibration plot; utility by decision curve analysis and instability by performance instability plots. Internal validation was carried out using 500 repetitions of bootstrap-resampling.

RESULTS

Dataset included 2222 LDL-C and 5149 non-HDL-C investigations, 303 were classed as elevated LDL-C (13.6%) and 1013 as elevated non-HDL-C cases (19.7%). Two predictors, gender and metabolic age, were identified in the LDL-C model with AuROC 0.639 (95% CI 0.617 to 0.661), poor calibration, and utility in the 7%-25% probability range. Three predictors-gender, diastolic blood pressure and metabolic age-were identified in the non-HDL-C model with AuROC 0.722 (95% CI 0.705 to 0.738), good calibration and utility in 9%-55% probability range.

DISCUSSION AND CONCLUSION

Overall results demonstrated acceptable discrimination for non-HDL-C model but inadequate performance of LDL-C model for clinical practice. An external validation study should be planned for non-HDL-C model.

Application of Generative Artificial Intelligence in Dyslipidemia Care

Dyslipidemia dramatically increases the risk of cardiovascular diseases, necessitating appropriate treatment techniques. Generative AI (GenAI), an advanced AI technology that can generate diverse content by learning from vast datasets, provides promising new opportunities to address this challenge. GenAI-powered frequently asked questions systems and chatbots offer continuous, personalized support by addressing lifestyle modifications and medication adherence, which is crucial for patients with dyslipidemia. These tools also help to promote health literacy by making information more accessible and reliable. GenAI helps healthcare providers construct clinical case scenarios, training materials, and evaluation tools, which supports professional development and evidence-based practice. Multimodal GenAI technology analyzes food images and nutritional content to deliver personalized dietary recommendations tailored to each patient's condition, improving long-term nutritional management for those with dyslipidemia. Moreover, using GenAI for image generation enhances the visual quality of educational materials for both patients and professionals, allowing healthcare providers to create real-time, customized visual aids. To apply successfully, healthcare providers must develop GenAI-related abilities, such as prompt engineering and critical evaluation of GenAI-generated data.

Socioeconomic inequalities and dyslipidaemia in adult population of the Ravansar Non-Communicable Disease Cohort Study: the role of sex and age

OBJECTIVES

This study represents a pioneering attempt to quantify the contribution of age, sex and socioeconomic status (SES) to the observed inequalities in lipid profile components.

DESIGN

Cross-sectional study.

SETTING

The data from the Ravansar Non-Communicable Disease (RaNCD) Cohort Study were used.

PARTICIPANTS

10 000 individuals aged 35-65 years.

MAIN OUTCOME MEASURES

Principal component analysis was used to determine the SES of individuals. Using the concentration index (C-index) and curves, the study assessed socioeconomic inequalities in dyslipidaemia in different age groups and genders. Decomposition analysis was used to determine the contribution of sex, age and SES to the observed inequality in the prevalence of dyslipidaemia components between the wealthiest and poorest groups.

RESULTS

The prevalence of dyslipidaemia was 72.39% of the population and was significantly higher in women than in men (excluding hypertriglyceridaemia). Overall, no significant SES-based inequality in dyslipidaemia was observed (C-index=-0.045, p=0.116), but after adjustment for age and sex, individuals with high SES had increased odds of dyslipidaemia (OR=1.16, 95% CI: 1.03 to 1.31). Hypercholesterolaemia and hyper-low-density lipoprotein (LDL) were more common in individuals with lower SES (C-index=-0.117 and -0.105), while hypo-high-density lipoprotein (HDL) was more prevalent in individuals with higher SES (C-index=0.029), regardless of adjustment for age, sex and confounding factors. SES played a significant role in hypercholesterolaemia and hyper-LDL (322.11% and 400.14%), while sex dominated in hypertriglyceridaemia and hypo-HDL (814.05% and -615.26%) and contributed to the existing inequalities.

CONCLUSION

The results highlight the existing inequalities in lipid profiles due to SES, sex and age. Consideration of these factors in interventions and policy decisions is critical to reduce abnormalities and inform future interventions.

The biotoxic effects of heavy metals exposure in miners and non-miners

Since little is known about the comparison of the biotoxic effects of heavy metals exposure on biochemical and hematological parameters in miners and non-miners, the current study aimed to compare the effects of arsenic (As), lead (Pb), and copper (Cu) in both groups. Demographic information and blood samples were collected from all participants and measures of As, Pb and Cu were obtained using Atomic Absorption Spectrophotometry. As and Pb mean concentrations in miners and Cu concentration were greater in non-miners. Miners also showed significantly higher level of RBC, HBG and HCT. In the adjusted model, cholesterol showed a positively association with Pb and Cu levels. Triglycerides, GGT, ALP, WBC and PLT positively and HDL-cholesterol negatively were associated with Cu. Creatinine was negatively associated with group variable. In conclusion, miners' high blood heavy metals concentrations can impact biochemical and hematological indices. These observations make monitoring of heavy metals necessary in miners.

Diet-related inflammation is positively associated with atherogenic indices

Current evidence suggests that non-traditional serum lipid ratios are more effective than traditional serum lipid parameters in predicting vascular diseases, and both of them are associated with dietary patterns. Therefore, this study aimed to investigate the relationship between the dietary inflammatory index (DII) and atherogenic indices using traditional serum lipid parameters (triglyceride (TG), total cholesterol (TC), LDL cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c)) and non-traditional serum lipid ratios (atherogenic index of plasma (AIP), Castelli's index-I (CRI_I), Castelli's index-II (CRI_II), the lipoprotein combination index (LCI), and the atherogenic coefficient (AC)). Basic information from the Ravansar Non-Communicable Diseases cohort study was utilized in the present cross-sectional observational study. The study included 8870 adults aged 35-65 years. A validated food frequency questionnaire (FFQ) was used to measure DII. We compared the distributions of outcomes by DII score groups using multivariable linear regression. The difference between DII score groups was evaluated by the Bonferroni test. The mean ± SD DII was - 2.5 ± 1.43, and the prevalence of dyslipidemia was 44%. After adjusting for age, sex, smoking status, alcohol consumption status, physical activity, systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood sugar (FBS), body mass index (BMI) and socioeconomic status (SES), participants in the highest quartile of DII had a greater risk for CRI_I (β = 0.11, CI 0.05, 0.18), CRI_II (β = 0.06, CI 0.01, 0.11), LCI (β = 0.11, CI 288.12, 8373.11), AC (β = 0.11, CI 0.05, 0.17) and AIP (β = 0.06, CI 0.02, 0.10). Moreover, according to the adjusted logistic regression model, the risk of dyslipidemia significantly increased by 24% (OR: 1.24, 95% CI 1.08-1.41), 7% (OR: 1.07, 95% CI 0.94, 1.21) and 3% (OR: 1.03, 95% CI 0.91, 1.16) in Q4, Q3 and Q2 of the DII, respectively. Finally, diet-related inflammation, as estimated by the DII, is associated with a higher risk of CRI-I, CRI-II, LCI, AC, and AIP and increased odds of dyslipidemia.

Dietary amino acid profile and risk of hypertension: findings from the Ravansar cohort study

INTRODUCTION

Hypertension (HTN) is a significant global health concern associated with morbidity and mortality. Recent research has explored the potential relationship between dietary protein intake and the development of HTN. This study aims to investigate the association between dietary amino acids and the incidence of HTN.

METHODS

This nested case-control study utilized data from the Ravansar Non-Communicable Disease (RaNCD) Cohort Study. The study included 491 new HTN cases identified over a 6-year follow-up period. For each case, four controls were randomly selected through density sampling. A food frequency questionnaire (FFQ) consisting of 125 food items was used to calculate dietary amino acid intake. HTN was determined based on systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg and/or current use of antihypertensive medication in subjects without pre-existing HTN at the start of the cohort study. Conditional logistic regression was used to estimate crude and adjusted odds ratios for HTN risk.

RESULTS

The median intake of all amino acids was lower in patients with HTN compared to the control group. After adjusting for various variables in different models, the risk of developing HTN tended to increase with higher dietary amino acid intake (excluding tryptophan and acidic amino acids). Specifically, individuals in the third tertile had a higher risk of developing new HTN than those individuals in the lowest tertile, although this difference was not statistically significant (P > 0.05).

CONCLUSION

The findings suggest that there may be an association between increased dietary amino acid intake and the risk of developing HTN, although this association was not statistically significant in this study. Further investigations in diverse populations are needed to explore the relationship between amino acids and HTN, as well as to determine the potential positive and negative effects of specific amino acid patterns on hypertension.

The association between dietary amino acid profile and the risk of type 2 diabetes: Ravansar non-communicable disease cohort study

BACKGROUND

Type 2 diabetes (T2D) is one of the most common chronic diseases and the main risk factors for T2D consist of a combination of lifestyle, unhealthy diet, and genetic factors. Amino acids are considered to be a major component of dietary sources for many of the associations between dietary protein and chronic disease. Therefore, this study amied to determine the association between dietary amino acid intakes and the incidence of T2D.

METHODS

The present nested case-control study was conducted using data from the Ravansar Non-Communicable Disease (RaNCD) Cohort Study. The information required for this study was collected from individuals who participated in the Adult Cohort Study from the start of the study until September 2023. Over a 6-year follow-up period, data from 113 new T2D cases were available. Four controls were then randomly selected for each case using density sampling. Cases and controls were matched for sex and age at the interview. Food frequency questionnaire (FFQ) was used to collect data related to all amino acids including tryptophan, threonine, isoleucine, leucine, lysine, methionine, cysteine, phenylalanine, tyrosine, valine, arginine, histidine, alanine, aspartic acid, glutamic acid, glycine, proline, and serine were also extracted. Binary logistic regression was used to estimate the crude and adjusted odds ratio for the risk of T2D.

RESULTS

Using the univariable model, a significant association was found between T2D risk and branched-chain, alkaline, sulfuric, and essential amino acids in the fourth quartile. Accordingly, individuals in the fourth quartile had a 1.81- to 1.87-fold higher risk of developing new T2D than individuals in the lowest quartile (P<0.05). After adjustment for several variables, the risk of developing a new T2D was 2.70 (95% CI: 1.16-6.31), 2.68 (95% CI: 1.16-6.21), 2.98 (95% CI: 1.27-6.96), 2.45 (95% CI: 1.02-5.90), and 2.66 (95% CI: 1.13-6.25) times higher, for individuals in the fourth quartile of branched-chain, alkaline, sulfuric, alcoholic, and essential amino acids compared with those in the lowest quartile, respectively.

CONCLUSIONS

The results showed that the risk of developing a new T2D was higher for individuals in the fourth quartile of branched-chain amino acids, alkaline, sulfate, and essential amino acids than in the lower quartile.

Major dietary patterns and dietary inflammatory index in relation to dyslipidemia using cross-sectional results from the RaNCD cohort study

Dyslipidemia can increase the risk of heart attack and stroke due to the restriction of blood flow through the blood vessels. Dietary modification is an appropriate approach to reducing this phenomenon. This cross-sectional study aimed to evaluate major dietary patterns and the dietary inflammatory index (DII) in relation to dyslipidemia. 5954 participants in the Ravansar non-communicable diseases (RaNCD) cohort study were eligible for this study. Dyslipidemia was diagnosed based on the lipid profile under consideration of the RaNCD physician. Dietary patterns were assessed by principal component analysis. The three identified dietary patterns included (1) plant-based pattern; (2) high protein and sugar pattern; and (3) energy-dense dense pattern. DII was also calculated based on the dietary information from a validated semi-quantitative food frequency questionnaire (FFQ). We found that higher adherence to DII was significantly associated with increased odds of dyslipidemia after adjusting for age, sex, and physical activity (OR: 1.24; CI 95% 1.09-1.42). Additionally, higher adherence to the high protein and sugar diet and an energy-dense diet was significantly associated with higher odds for dyslipidemia (OR: 1.31; CI 95% 1.16-1.49) and (OR: 1.28; CI 95% 1.12-1.46). Nevertheless, according to our results, following plant-based diet had no association with dyslipidemia in both crude and adjusted models. Our findings revealed that greater adherence to DII, a high-protein, high-sugar diet, and an energy-dense diet can have undesirable effects on dyslipidemia.

Process mining and data mining applications in the domain of chronic diseases: A systematic review

The widespread use of information technology in healthcare leads to extensive data collection, which can be utilised to enhance patient care and manage chronic illnesses. Our objective is to summarise previous studies that have used data mining or process mining methods in the context of chronic diseases in order to identify research trends and future opportunities. The review covers articles that pertain to the application of data mining or process mining methods on chronic diseases that were published between 2000 and 2022. Articles were sourced from PubMed, Web of Science, EMBASE, and Google Scholar based on predetermined inclusion and exclusion criteria. A total of 71 articles met the inclusion criteria and were included in the review. Based on the literature review results, we detected a growing trend in the application of data mining methods in diabetes research. Additionally, a distinct increase in the use of process mining methods to model clinical pathways in cancer research was observed. Frequently, this takes the form of a collaborative integration of process mining, data mining, and traditional statistical methods. In light of this collaborative approach, the meticulous selection of statistical methods based on their underlying assumptions is essential when integrating these traditional methods with process mining and data mining methods. Another notable challenge is the lack of standardised guidelines for reporting process mining studies in the medical field. Furthermore, there is a pressing need to enhance the clinical interpretation of data mining and process mining results.

Serum liver enzymes and metabolic syndrome from the Rafsanjan Cohort Study

Our investigation aimed at evaluating the relationship between metabolic syndrome, alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma glutamyl transferase (GGT), and alkaline phosphatase (ALP) in the Rafsanjan cohort study (RCS). We used data obtained from the RCS, as a part of the prospective epidemiological research studies in Iran. In this cross-sectional research, 9895 participants from the baseline phase of RCS who completed medical questionnaire were included. Metabolic syndrome (MetS) defined using NCEP-ATP III criteria. The relationship between elevated serum liver enzymes levels even within the normal range and metabolic syndrome was evaluated by logistic regressions. The prevalence of MetS was 34.42% in the participants of study. The mean concentrations of AST, ALT, ALP, and GGT increased with increasing MetS components. After adjusting for all potential confounders, elevated serum concentrations of ALT, AST, GGT, and ALP even within the normal range were related with an increased odds of MetS. MetS was associated with increased levels of liver enzymes even within the normal range. These results indicated the potential for elevated liver enzymes as biomarkers for the possible presence of MetS.

Pro-inflammatory diet, cardio-metabolic risk factors and risk of type 2 diabetes: A cross-sectional analysis using data from RaNCD cohort study

BACKGROUND

Inflammation and cardiometabolic risk factors can be involved in developing type 2 diabetes mellitus (T2DM). This study aimed to investigate and compare the association between a pro-inflammatory diet and cardiometabolic risk factors in patients with T2DM and non-T2DM cases.

METHODS

In this cross-sectional population-based study, considering the baseline data of the Ravansar Non-Communicable Disease (RaNCD) cohort, patients with T2DM (n = 785) and non-T2DM cases (n = 8254) were included. The dietary inflammatory index (DII) was calculated using a food frequency questionnaire (FFQ) and was classified into four groups (quartiles) with lowest to highest scores. Logistic regression analysis was conducted to determine the association between DII and cardiometabolic risk factors in both groups.

RESULTS

The participants were 9,039 (4140 men and 4889 women) with a mean age of 47.4 ± 8.2 years; the mean body mass index (BMI) and DII were 27.49 ± 4.63 kg/m² and - 2.49 ± 1.59, respectively. After adjustment for confounding factors, we found that DII can increase the risk of T2DM by 61% (95% CI 1.27 to 2.05, P < 0.001). A comparison of two groups revealed that the association of DII, obesity/overweight and dyslipidemia were also significant in both diabetic (P < 0.05) and non-diabetic cases (P < 0.05). However, no significant association was found between DII, MetS, and hypertension in either of the groups. The association between DII and cardiovascular diseases (CVDs) was only significant in diabetic patients (1.65; 95%CI: 1.02 to 2.65, P = 0.04) and T2DM showed an interaction with the association between DII and CVDs.

CONCLUSION

Inflammatory potential of diet may increase the risk of T2DM. Although it can increase the risk of some cardiometabolic risk factors in both diabetic and non-diabetic cases, its effects were greater among patients with T2DM. However, further prospective studies are required to confirm these associations.

Metabolic obesity phenotypes and chronic kidney disease: a cross-sectional study from the RaNCD cohort study

BACKGROUND

Investigating the effect of metabolic disorders on chronic kidney disease (CKD) in the presence or the absence of obesity is of great importance. This study aimed to examine the independent and joint relationships of obesity and metabolic syndrome (MetS) with CKD.  METHODS : The present study was performed on 9,762 participants from the baseline phase of the Ravansar non- communicable diseases (RaNCD) study. Thereafter, the CKD was estimated by glomerular filtration rate (eGFR) using the Modification of Diet in Renal Disease (MDRD) equation. All the included participants were categorized into the following four phenotypes: metabolically healthy non-overweight/obesity (MHNO), metabolically unhealthy non-overweight/obesity (MUNO), metabolically healthy overweight/obesity (MHO), and metabolically unhealthy overweight/obesity (MUO). Finally, Logistic regression analysis was used to estimate the odds ratio (ORs).

RESULTS

The mean age of the included participants was 47.33 ± 8.27 years old, %48.16 (4,701) of whom were men. As well, 1,058(10.84%) participants had CKD (eGFR less than 60 ml/min/1.73m2). The overweight/obesity was not significantly associated with odds of CKD. The odds of CKD in male subjects with MetS was 1.48 times higher than non-MetS ones (95% CI: 1.10, 2.01). After adjusting the confounders, the odds of CKD were 1.54 times (95% CI: 1.12, 2.11) higher in the MUNO and 2.22 times (95% CI: 1.44, 3.41) higher in the MUO compared to MHNO phenotype in male subjects. The odds of CKD in the MUNO and MUO was 1.31 times (95% CI: 1.10, 1.60) and 1.23 times (95% CI: 1.01, 1.54) higher than MHNO phenotype in female subjects, respectively.

CONCLUSION

The odds of CKD were higher in MUNO and MUO phenotypes. Therefore, lifestyle modification is recommended to control normal weight and healthy metabolism.

Evaluation of Relationship between Serum Liver Enzymes and Hypertension: A Cross-Sectional Study Based on Data from Rafsanjan Cohort Study

Background. Hypertension as a major risk factor for cardiovascular diseases is among the leading causes of death worldwide. The relationship between elevated serum levels of liver enzymes and hypertension has been reported in limited studies, and to the best of our knowledge, there are no previous reports in the literature on this issue in the southeast of Iran. Our investigation aimed at evaluating the relation between ALT, AST, GGT, and ALP with hypertension in the Rafsanjan Cohort Study, a city in Kerman Province, Iran. Methods. In this cross-sectional study, we used data obtained from the Rafsanjan Cohort Study (RCS), as a part of the prospective epidemiological research studies in Iran (PERSIAN). The association of the liver enzymes levels with hypertension was investigated using the multivariable logistic regression models. Results. Among 9930 participants, the mean age (±SD) was 49.94 (±9.56) years, and 46.56% were men. The odds of abnormal blood pressure significantly increased along with the higher levels of ALT, GGT, and ALP which remained significant only for ALP after adjustment for all confounding variables in both males and females (OR in males: 1.36, 95% CI = 1.09–1.69, OR in females: 1.25, 95% CI = 1.01–1.54). In subjects with normal levels of ALT, AST, GGT, and ALP, dose-response increases were observed for abnormal blood pressure in both genders. Finally, we found that, among liver enzymes, only elevated ALP was significantly correlated with the odds of stage 1 hypertension and stage 2 hypertension for both genders. Conclusions. In subjects with normal levels of ALT, AST, GGT, and ALP, dose-response increases were observed for abnormal blood pressure in both genders. Increased serum ALP activity was positively associated with increased odds of hypertension in males and females. Therefore, increased ALP could be an early indicator of hypertension.

Influence of Omega-3 Fatty Acid-Rich Fish Oils on Hyperlipidemia: Effect of Eel, Sardine, Trout, and Cod Oils on Hyperlipidemic Mice

Dyslipidemia is one of the most important cardiovascular disease (CVD) risk factors. Polyunsaturated fatty acids (FAs), and especially omega-3 FAs, could significantly contribute to the management of dyslipidemia and the prevention of CVD. The anti-hyperlipidemic effect of selected fish oils (eel, sardine, trout, cod liver) was comparatively evaluated in a high fat diet (HFD)-fed mouse model. At the end of 30 days on the HFD, all animals were hyperlipidemic and were switched to a diet consisting of 90% standard rodent chow plus 10% of oil from eel, sardine, cod liver, or trout. At the end of 60 days on these diets, blood glucose, total blood cholesterol, triglycerides (TGs), and high density lipoprotein (HDL) were quantitated. All diets, except sardine and standard rodent chow, showed statistically significant decreases in blood glucose from day 30 to 90. Total blood cholesterol decreased in all diets except the HFD group, which was continued on this diet until the end of the study. Eel and cod liver oil diets showed significant decreases in TGs. All dietary groups showed a decrease in HDL, but only the trout and standard chow groups exhibited statistically significant decreases. The fish oils tested here for effects on hyperlipidemia vary in per cent of omega-3 FAs and omega-6/-3 FA ratios as determined by gas chromatography Overall, smoked eel was the best source of omega-3 FA, with a balance of omega-6 FA, that ameliorated HFD-induced mixed hyperlipidemia.