Appropriate Total cholesterol cut-offs for detection of abnormal LDL cholesterol and non-HDL cholesterol among low cardiovascular risk population

Nantsupawat, Nopakoon; Booncharoen, Apaputch; Wisetborisut, Anawat; Jiraporncharoen, Wichuda; Pinyopornpanish, Kanokporn; Chutarattanakul, Lalita; Angkurawaranon, Chaisiri

doi:10.1186/s12944-019-0975-x

Cited by 27 publications

(23 citation statements)

References 27 publications

(21 reference statements)

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“…We also wanted to see if the concentration estimates generated from both plasma and DBS samples were accurate enough to be used in clinical practice. Knowing a patients exact concentration of TriG, HDL, LDL or total cholesterol is less important than knowing their relative concentration ( https://www.nhs.uk/conditions/high-cholesterol/cholesterol-levels/ ; Nantsupawat et al 2019 ), i.e. if they fall outside of the healthy range, with a well-defined set of cut-offs described for each of the four ‘clinical lipids’ ( https://www.nhs.uk/conditions/high-cholesterol/cholesterol-levels/ ; Nantsupawat et al 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…Knowing a patients exact concentration of TriG, HDL, LDL or total cholesterol is less important than knowing their relative concentration ( https://www.nhs.uk/conditions/high-cholesterol/cholesterol-levels/ ; Nantsupawat et al 2019 ), i.e. if they fall outside of the healthy range, with a well-defined set of cut-offs described for each of the four ‘clinical lipids’ ( https://www.nhs.uk/conditions/high-cholesterol/cholesterol-levels/ ; Nantsupawat et al 2019 ). In plasma our estimated concentrations classified people into the correct clinical category with accuracies of 83.9%, 64.9%, 82.2%, and 64.9% for triglyceride, HDL, LDL and total cholesterol respectively.…”

Section: Discussionmentioning

confidence: 99%

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Combining lipidomics and machine learning to measure clinical lipids in dried blood spots

et al. 2020

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Introduction Blood-based sample collection is a challenge, and dried blood spots (DBS) represent an attractive alternative. However, for DBSs to be an alternative to venous blood it is important that these samples are able to deliver comparable associations with clinical outcomes. To explore this we looked to see if lipid profile data could be used to predict the concentration of triglyceride, HDL, LDL and total cholesterol in DBSs using markers identified in plasma. Objectives To determine if DBSs can be used as an alternative to venous blood in both research and clinical settings, and to determine if machine learning could predict 'clinical lipid' concentration from lipid profile data. Methods Lipid profiles were generated from plasma (n = 777) and DBS (n = 835) samples. Random forest was applied to identify and validate panels of lipid markers in plasma, which were translated into the DBS cohort to provide robust measures of the four 'clinical lipids'. Results In plasma samples panels of lipid markers were identified that could predict the concentration of the 'clinical lipids' with correlations between estimated and measured triglyceride, HDL, LDL and total cholesterol of 0.920, 0.743, 0.580 and 0.424 respectively. When translated into DBS samples, correlations of 0.836, 0.591, 0.561 and 0.569 were achieved for triglyceride, HDL, LDL and total cholesterol. Conclusion DBSs represent an alternative to venous blood, however further work is required to improve the combined lipidomics and machine learning approach to develop it for use in health monitoring.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Combining lipidomics and machine learning to measure clinical lipids in dried blood spots

et al. 2020

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“…For example, low-density lipoprotein cholesterol (LDL-C) is considered a more sensitive marker for determination of atherosclerotic cardiovascular disease risk, though in most LMICs total cholesterol tests are preferred due to their low cost and availability. 59 , 60 For ease of data collection, commonly recognized definitions and classifications, as outlined in international guidelines, will be used in the MAPS approach. In addition, we considered pharmacotherapy as a measure of treatment in NCD patients.…”

Section: Discussionmentioning

confidence: 99%

A Methodology for Mapping the Patient Journey for Noncommunicable Diseases in Low- and Middle-Income Countries

Bharatan¹,

Duddi²,

Huang³

et al. 2021

JHL

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Noncommunicable diseases (NCDs) are responsible for 71% of all worldwide mortality each year, and have an exceptionally large impact in low- and middle-income countries (LMICs). However, there is often a lack of local data from these countries to inform practice and policy improvements. Generating locally contextualized evidence base for NCDs that can help identify gaps, aid decision-making and improve patient care in LMICs needs an innovative approach. The approach used in Mapping the Patient Journey Towards Actionable Beyond the Pill Solutions (MAPS) is designed to quantitatively map different stages of the patient journey in four critical NCDs, ie, hypertension, dyslipidemia, depression, and pain (chronic and neuropathic) across selected LMICs in Africa, the Middle East, South East Asia, and Latin America. The key touchpoints along the patient journey include awareness, screening, diagnosis, treatment, adherence, and control or remission. MAPS employs an evidence mapping methodology that follows a three-step semi-systematic review: 1) systematic peer-reviewed database search; 2) unstructured searches of local or real-world data; and 3) expert opinion. Evidence generation and visualization is based on locally validated and deduplicated data published over the last 10 years. This approach will be the first to provide quantitative mapping of the different stages of the patient journey for selected NCDs in LMICs. By focusing on local, patient-centric data, the goal of the MAPS initiative is to address and prioritize local research and knowledge gaps, then contribute to evidence-based, high-quality, and affordable advances in the management of NCDs in LMICs. This will ultimately improve patient outcomes and contribute towards the achievement of global NCD targets.

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“…Non-HDL cholesterol (non-HDL-C), when compared to LDL cholesterol, serves as a superior marker for cardiovascular disease risk [26] with elevated levels indicating worse outcomes [27,28]. Gamma-glutamyl transferase (GGT), which is present in several cell types, can be used as a marker of oxidative stress [29,30]-a state in which pro-oxidants overwhelm antioxidants and free reactive oxygen species (ROS)-and induce damage to physiological systems [31].…”

Section: Introductionmentioning

confidence: 99%

Chronic Stress and Cardiovascular Disease among Individuals Exposed to Lead: A Pilot Study

Obeng-Gyasi

2020

Diseases

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Chronic stress and cardiovascular disease risk were explored in a predominately middle-aged adult population exposed to elevated lead levels in this cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) from the period 2007-2010. Elevated lead exposure was defined using the epidemiological threshold of a blood lead level (BLL) > 5 μg/dL as defined by the U.S. Centers for Disease Control and Prevention (CDC). Allostatic load (AL), a measure of chronic stress, was operationalized using 10 clinical markers. The geometric mean values for clinical cardiovascular disease risk markers of interest (a) Gamma glutamyl-transferase (GGT) (a marker of oxidative stress), and (b) non-HDL cholesterol (non-HDL-c) (a marker of cardiovascular disease risk) were explored among lead-exposed and less lead-exposed individuals with differential chronic stress (AL) levels. Associations between AL and GGT/non-HDL-C were analyzed using linear regression models. The likelihood of increased clinical markers in lead-exposed individuals with high compared to low AL was explored using binary logistic regression models. In analyzing lead-exposed as compared to less lead-exposed populations, the geometric mean of the variables of interest showed significant elevations among lead-exposed individuals as compared to less lead-exposed individuals. Simple linear regression revealed that AL was positively associated with the variables of interest among the lead-exposed. In binary logistic regression among the lead-exposed, those with high AL, as compared to those with low AL, had significantly higher odds of having elevated non-HDL-C. This study submits that those exposed to lead with increasing AL may experience adverse cardiovascular health outcomes.

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Appropriate Total cholesterol cut-offs for detection of abnormal LDL cholesterol and non-HDL cholesterol among low cardiovascular risk population

Cited by 27 publications

References 27 publications

Combining lipidomics and machine learning to measure clinical lipids in dried blood spots

Combining lipidomics and machine learning to measure clinical lipids in dried blood spots

A Methodology for Mapping the Patient Journey for Noncommunicable Diseases in Low- and Middle-Income Countries

Chronic Stress and Cardiovascular Disease among Individuals Exposed to Lead: A Pilot Study

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