HDL Human

What is the basic composition of HDL particles and how does this relate to their function?

HDL (high-density lipoprotein) particles consist of a combination of fat (lipid) and protein components. The lipids must be attached to proteins to facilitate movement through the bloodstream. HDL contains a higher proportion of protein compared to other lipoproteins, giving it greater density . The primary protein component is apolipoprotein A-I, which plays a crucial role in HDL's ability to remove cholesterol from peripheral tissues.

The main function of HDL is transporting cholesterol from peripheral tissues back to the liver, which then removes the cholesterol from the body . This reverse cholesterol transport mechanism explains why HDL is often referred to as "good" cholesterol, as it helps remove excess cholesterol that might otherwise accumulate in arterial walls . HDL's composition directly enables this function - the specific proteins and lipids create a structure capable of accepting and transporting cholesterol molecules.

How do different isolation methods affect experimental outcomes when studying HDL subclasses?

The isolation method chosen for HDL research significantly impacts experimental outcomes and interpretations. Studies show that HDL can be separated into distinct subpopulations, including the two major HDL subclasses: HDL₂ (larger, less dense) and HDL₃ (smaller, denser) . These subclasses demonstrate different metabolic effects in experimental systems.

When designing studies, researchers must consider:

• Ultracentrifugation-based methods: The traditional approach separating HDL by density, potentially altering native HDL structure through high centrifugal forces

• Size-exclusion methods: Separate based on particle size rather than density

• Immunoaffinity techniques: Isolate specific apolipoprotein-containing HDL particles

• Precipitation methods: Commonly used in clinical settings but less precise for research

The isolation method directly affects the functional properties observed in subsequent experiments. For instance, research on skeletal muscle metabolism showed that isolated HDL₂ and HDL₃ subclasses differentially affected fatty acid oxidation and glucose metabolism . Researchers must report detailed isolation protocols and consider how isolation techniques might affect their specific functional assays.

What advanced approaches can distinguish between HDL particle number, size, and functionality in human samples?

Moving beyond simple HDL cholesterol concentration measurements requires sophisticated analytical approaches:

• Nuclear Magnetic Resonance (NMR) spectroscopy: Provides information on HDL particle numbers and size distribution without physical separation of particles, allowing for high-throughput analysis of clinical samples

• Mass spectrometry-based proteomics and lipidomics:

  • Characterizes the complex protein and lipid composition of HDL

  • Identifies specific bioactive components associated with functionality

  • Reveals population heterogeneity in HDL composition

• Functional assays:

  • Cholesterol efflux capacity measurements: Quantify the ability of HDL to accept cholesterol from cells

  • Antioxidant capacity: Measure HDL-associated enzyme activities like paraoxonase 1 (PON1)

  • Anti-inflammatory potential: Assess HDL's ability to inhibit inflammatory signaling

  • Metabolic effects: Examine HDL's impact on cellular energy metabolism

• Single-particle analysis techniques:

  • Atomic force microscopy

  • Electron microscopy

  • Fluorescence-based particle tracking

These advanced approaches provide complementary information, and researchers should select methods appropriate for their specific research questions, while recognizing the limitations of each technique. The integration of multiple analytical approaches offers the most comprehensive assessment of HDL status in human samples.

What considerations are important when selecting animal models for human HDL research?

Animal models present both opportunities and limitations for translational HDL research. Key considerations include:

• Species-specific differences in HDL metabolism:

  • Mice and rats naturally lack cholesteryl ester transfer protein (CETP), a key enzyme in human HDL metabolism

  • Rodents have predominantly HDL-dominant lipoprotein profiles unlike the LDL-dominant profile in humans

  • As demonstrated in study , HDL subclasses enhanced fatty acid oxidation in human myotubes but improved anaerobic metabolism in mouse myotubes, highlighting critical species differences

• Experimental design factors:

  • Group sizing and statistical power (studies typically use at least 5 animals per group as seen in )

  • Duration of interventions (studies examining HDL function often require several months, as in the 4-month intervention period used in )

  • Appropriate controls and intervention dosing

• Common rodent models:

  • Wild-type models for basic research

  • Genetically modified models (transgenic, knockout)

  • Diet-induced models like the high-fat diet model described in

• Validation approaches:

  • Confirmation in human cell systems

  • Comparative studies across multiple model systems

  • Translation to human observational data

The experimental design used in search result demonstrates a comprehensive approach with Sprague Dawley rats divided into three groups (control diet, high-fat diet, high-fat diet + selenium) with appropriate sample size (five animals per group) and intervention duration (4 months).

How should researchers design cell-based systems to study HDL's effects on human metabolism?

Cell-based systems offer controlled environments for mechanistically investigating HDL's metabolic effects. Based on research methodologies from the literature, optimal design includes:

• Cell type selection:

  • Primary human cells provide greater translational relevance

  • Cell lines offer experimental consistency but may not fully recapitulate human responses

  • The research in used both differentiated mouse myotubes and primary human skeletal muscle myotubes, enabling direct species comparisons

• Experimental conditions:

  • HDL concentration range (physiologically relevant)

  • Exposure duration (acute vs. chronic effects)

  • Substrate availability (glucose, fatty acids, amino acids)

  • Hormonal context (insulin, glucagon)

• Comprehensive functional readouts:

  • Substrate uptake measurements (glucose, fatty acids)

  • Metabolic flux analysis:

    • Oxygen consumption rate for mitochondrial respiration

    • Extracellular acidification rate for glycolysis

    • Substrate oxidation using labeled precursors

  • Gene expression analysis for metabolic pathways

  • Protein expression and phosphorylation status

• Controls and validation:

  • Appropriate vehicle controls

  • Positive control compounds with known effects

  • Confirmation across multiple experimental systems

The methodology employed in study exemplifies this approach, examining HDL subclass effects on multiple aspects of energy metabolism (glucose uptake, fatty acid oxidation, gene expression) in both mouse and human cell systems, revealing important species-specific responses to HDL exposure.

What methodological strategies can address the gap between in vitro HDL functionality and in vivo relevance?

Bridging the gap between in vitro observations and in vivo relevance represents a significant challenge in HDL research. Several methodological strategies can help address this disconnect:

• Multisystem validation approach:

  • Parallel studies in cell culture, animal models, and human samples

  • Ex vivo testing of HDL isolated from in vivo intervention studies

  • Correlation of in vitro functional measures with in vivo endpoints

• Physiologically relevant in vitro conditions:

  • Using primary human cells rather than transformed cell lines

  • Studying HDL functionality in three-dimensional cell culture systems

  • Co-culture systems incorporating multiple cell types

  • Dynamic flow conditions mimicking vascular environments

• Innovative animal models:

  • Humanized lipoprotein profile models

  • Tissue-specific transgenic approaches

  • Conditional knockout systems for temporal control

• Translational human studies:

  • Intervention studies measuring both HDL quantity and quality

  • Mendelian randomization approaches for causal inference

  • Biobanking with comprehensive functional characterization

• Integration of multiple functional parameters:

  • Combining different HDL functional assays into composite scores

  • Multivariate analysis approaches to identify patterns

  • Systems biology modeling of HDL metabolism and function

Study demonstrated this concept by examining both biochemical measures (PON1 and PAF-AH activity) and physiological outcomes in an animal model, providing stronger evidence for functional relevance than in vitro enzyme studies alone.

How do HDL subclasses differentially affect energy metabolism in human tissues?

Research demonstrates that HDL subclasses exert profound and specific effects on cellular energy metabolism. As shown in study , HDL₂ and HDL₃, the two major HDL subclasses, modulate energy metabolism in skeletal muscle cells with distinct patterns:

• In human myotubes:

  • Both HDL₂ and HDL₃ attenuated glucose metabolism

  • Both subclasses markedly increased fatty acid uptake and oxidation

  • HDL exposure upregulated mRNA expression of genes related to fatty acid metabolism

  • HDL induced incorporation of oleic acid into different lipid classes

• Species-specific differences:

  • In contrast to effects in human cells, HDL subclasses enhanced glycolysis in mouse myotubes

  • HDL₃ increased ATP-linked respiration upon glucose conditioning in mouse cells

  • HDL₂ improved complex I–mediated mitochondrial respiration upon fatty acid treatment in mouse cells

• Methodological considerations for studying these effects:

  • Cell-type specific responses require testing in relevant primary human tissues

  • Multiple metabolic parameters should be assessed simultaneously (substrate uptake, oxidation, gene expression)

  • Both acute and chronic HDL exposure should be examined

  • Concentration-dependent effects should be characterized

These findings support the emerging concept of HDL as a circulating modulator of energy metabolism, with implications for metabolic diseases beyond cardiovascular conditions. The exact mechanisms and components of HDL causing these metabolic effects require further investigation, particularly regarding potential differences between HDL subclasses.

What experimental approaches best capture HDL's influence on mitochondrial function?

Investigating HDL's effects on mitochondrial function requires sophisticated experimental approaches that can detect subtle but physiologically significant changes. Based on methodology described in the literature:

• Respirometry techniques:

  • Seahorse extracellular flux analysis allows real-time measurement of:

    • Basal respiration

    • ATP-linked respiration

    • Maximal respiratory capacity after uncoupling

    • Spare respiratory capacity

    • Non-mitochondrial oxygen consumption

  • Substrate-specific respiration testing reveals pathway-specific effects:

    • Complex I-mediated respiration (as examined in study )

    • Fatty acid oxidation capacity

    • Substrate preference switching

• Mitochondrial content and dynamics:

  • Mitochondrial DNA copy number quantification

  • Protein markers of mitochondrial content

  • Fusion/fission protein expression and localization

  • Morphological analysis via microscopy

• Functional outcome measurements:

  • ATP production assays

  • Reactive oxygen species generation

  • Membrane potential assessment

  • Calcium handling capacity

• Molecular mechanism investigations:

  • Transcriptional effects on mitochondrial genes

  • Post-translational modifications of respiratory complexes

  • Mitochondrial proteome analysis

  • Signaling pathway activation

Research such as that described in demonstrates the importance of examining multiple parameters simultaneously, as HDL had distinct effects on different aspects of mitochondrial function (ATP-linked respiration vs. complex I-mediated respiration) depending on substrate availability and HDL subclass.

How do HDL-associated enzymes contribute to HDL's metabolic effects?

• Paraoxonase 1 (PON1):

  • High-fat diet feeding significantly decreased PON1 activity (P < 0.001) and protein levels (P < 0.01) compared to control

  • Selenium supplementation significantly increased both PON1 activity (P < 0.01) and protein levels (P < 0.05) in high-fat diet fed animals

  • PON1 activity showed inverse correlation with reactive oxygen species (ROS) levels

  • PON1 contributes to HDL's antioxidant function, potentially protecting against lipid peroxidation

• Platelet-activating factor acetylhydrolase (PAF-AH):

  • High-fat diet feeding significantly decreased PAF-AH protein levels (P < 0.05)

  • PAF-AH helps detoxify oxidized phospholipids on HDL particles

  • Unlike PON1, selenium supplementation did not significantly affect PAF-AH levels

• Methodological approaches to study HDL-associated enzymes:

  • Activity measurements using specific substrates (paraoxon for PON1)

  • Protein quantification via ELISA or Western blotting

  • Correlation with oxidative stress markers

  • Intervention studies (nutritional, pharmacological)

• Experimental considerations:

  • Measuring both enzyme activity and protein levels provides complementary information

  • Environmental factors (diet, oxidative stress) significantly influence enzyme function

  • Genetic variants may affect baseline enzyme activity and response to interventions

The experimental approach in study demonstrates how dietary interventions can modulate HDL-associated enzymes, suggesting potential therapeutic strategies to enhance HDL functionality beyond merely increasing HDL-C levels.

What explains the U-shaped relationship between HDL cholesterol levels and mortality risk?

Recent observational studies have revealed a counterintuitive U-shaped association between HDL-C levels and mortality, where both very low and very high HDL cholesterol levels are associated with increased mortality risk . This finding challenges the traditional "higher is better" paradigm for HDL-C.

Several mechanisms may explain this U-shaped relationship:

• Dysfunctional HDL particles:

  • Extremely high HDL-C levels may reflect dysfunctional particles with impaired cholesterol efflux capacity

  • Alterations in HDL composition rather than concentration may be the determining factor

  • Oxidative or other modifications may affect HDL functionality at extreme levels

• Genetic determinants:

  • Genetic variants that substantially raise HDL-C may not confer cardiovascular protection

  • Some genetic causes of very high HDL-C may be associated with other health risks

• Reverse causality:

  • Some disease states may artificially elevate HDL-C levels

  • Certain medications or lifestyle factors affecting HDL-C may have independent effects on mortality

• Altered HDL subclass distribution:

  • Extreme HDL-C levels may reflect skewed distribution of HDL subclasses with varying functionality

  • The proportion of HDL₂ to HDL₃ may be more important than total HDL-C

This U-shaped relationship underscores the importance of studying HDL functionality and composition rather than focusing solely on HDL-C concentration. Research designs should include participants across the full spectrum of HDL-C levels and incorporate functional assessments alongside standard lipid measurements.

How should researchers design experiments to study HDL functionality during inflammatory conditions?

Inflammation significantly alters HDL composition and functionality. Designing experiments to study these changes requires careful methodological consideration:

• Experimental model selection:

  • Animal models of acute and chronic inflammation

    • High-fat diet models as used in study

    • Endotoxin challenge models

    • Disease-specific models (arthritis, lupus)

  • Cell-based inflammation models

    • Cytokine stimulation

    • Pattern recognition receptor activation

  • Human studies in inflammatory conditions

    • Acute infection

    • Chronic inflammatory diseases

    • Surgery or trauma

• Comprehensive HDL characterization:

  • Composition analysis:

    • Proteomics to detect acute-phase protein incorporation

    • Lipidomics to identify inflammatory lipid species

    • Oxidative modification assessment

  • Functional assays:

    • HDL-associated enzyme activities (PON1, PAF-AH) as measured in

    • Cholesterol efflux capacity

    • Anti-inflammatory capacity

    • Antioxidant potential

• Temporal considerations:

  • Time-course studies capturing the evolution of HDL changes

  • Acute vs. chronic inflammation effects

  • Resolution phase analysis

• Intervention design:

  • Anti-inflammatory treatments (as in selenium supplementation study )

  • Antioxidant therapies

  • HDL-targeted therapies during inflammation

• Translation to human disease:

  • Validation in patient samples

  • Correlation with inflammatory biomarkers

  • Consideration of medication effects

The experimental approach in provides a model for studying HDL functionality during metabolic inflammation, demonstrating that selenium supplementation can partially restore HDL-associated enzyme function compromised by high-fat diet feeding.

What experimental approaches can assess HDL's role in non-cardiovascular diseases?

Emerging evidence suggests HDL may play important roles in several non-cardiovascular diseases including infectious disease, autoimmune disease, cancer, type 2 diabetes, kidney disease, and lung disease . Investigating these relationships requires specific experimental approaches:

• Infectious disease models:

  • In vitro pathogen neutralization assays

  • Animal models of infection with HDL manipulation

  • Human observational studies correlating HDL parameters with infection outcomes

  • Mechanisms: HDL binding of endotoxin, direct antimicrobial properties, immune cell modulation

• Cancer research approaches:

  • Cell culture systems examining HDL effects on cancer cell proliferation

  • Animal tumor models with HDL intervention

  • HDL-mediated drug delivery systems

  • Mechanisms: Cholesterol metabolism in cancer cells, membrane signaling platforms, HDL-associated bioactive molecules

• Metabolic disease investigation:

  • Energy metabolism in skeletal muscle (as in )

  • Pancreatic beta-cell function studies

  • Hepatic glucose production models

  • Adipose tissue inflammation models

  • Mechanisms: Direct signaling through HDL receptors, lipid raft modulation, anti-inflammatory effects

• Kidney disease models:

  • Podocyte function studies

  • Glomerular filtration models

  • Proteinuria assessment

  • Mechanisms: Cholesterol homeostasis in kidney cells, anti-inflammatory protection, antioxidant effects

• Experimental design considerations:

  • Disease-specific endpoints relevant to pathophysiology

  • Appropriate timing of HDL intervention (preventive vs. therapeutic)

  • Assessment of both HDL quantity and quality

  • Tissue-specific HDL function analysis

These experimental approaches should incorporate both mechanistic in vitro studies and translational models to establish causal relationships between HDL function and non-cardiovascular diseases.

What high-throughput approaches can characterize HDL heterogeneity in large human cohorts?

Characterizing HDL heterogeneity in large human cohorts requires scalable, reproducible methodologies that capture the complexity of HDL particles while maintaining throughput:

• Advanced lipoprotein analysis technologies:

  • Nuclear magnetic resonance (NMR) spectroscopy:

    • Provides HDL particle concentration and size distribution

    • Allows subclass quantification without physical separation

    • Enables high-throughput analysis suitable for large cohorts

  • Ion mobility analysis:

    • Separates lipoprotein particles based on gas-phase mobility

    • Provides detailed size distribution information

  • Vertical auto profile (VAP) testing:

    • Single-spin density gradient ultracentrifugation

    • Measures cholesterol in various lipoprotein subfractions

• Mass spectrometry-based approaches:

  • Shotgun proteomics for HDL protein composition

  • Targeted proteomics for specific HDL-associated proteins

  • Lipidomics for comprehensive lipid profiling

  • Multiplexed selected reaction monitoring for protein quantification

• Functional high-throughput assays:

  • Plate-based cholesterol efflux capacity assays

  • Automated enzyme activity measurements (PON1, PAF-AH)

  • Cell-based reporter systems for HDL functions

• Data integration and analysis:

  • Machine learning algorithms to identify HDL signatures

  • Multivariate analysis of HDL parameters

  • Network analysis of HDL-associated proteins and lipids

  • Integration with genomic and clinical data

• Biobanking considerations:

  • Standardized sample collection and processing

  • Storage conditions preserving HDL integrity

  • Quality control measures for long-term studies

These approaches enable comprehensive characterization of HDL heterogeneity in population studies, allowing researchers to move beyond simple HDL-C measurements and better understand the relationship between HDL composition, functionality, and disease risk.

How can researchers effectively study HDL-tissue interactions in human experimental systems?

Studying HDL interactions with specific tissues presents methodological challenges, particularly in human experimental systems. Several approaches can address these challenges:

• Ex vivo human tissue systems:

  • Fresh tissue explants from surgical specimens

  • Precision-cut tissue slices maintaining 3D architecture

  • Isolated primary cells from specific tissues

  • Perfused organ systems (when ethically available)

• Advanced human cell culture models:

  • Primary human cell cultures as used in

  • Induced pluripotent stem cell (iPSC)-derived tissue models

  • Organoids recapitulating tissue architecture

  • Microfluidic organ-on-chip platforms

  • Co-culture systems modeling tissue complexity

• Analytical approaches for HDL-tissue interaction:

  • HDL particle uptake and binding studies

  • Transcriptional profiling after HDL exposure

  • Signaling pathway activation analysis

  • Metabolic flux analysis as demonstrated in

  • Proteomics and phosphoproteomics

  • Live-cell imaging of fluorescently labeled HDL

• Methodological considerations:

  • Use of physiologically relevant HDL concentrations

  • Comparison of multiple HDL subclasses

  • Time-course experiments capturing both acute and chronic effects

  • Consideration of tissue-specific metabolism and function

The study described in exemplifies this approach, using primary human myotubes to investigate HDL subclass effects on skeletal muscle energy metabolism, revealing tissue-specific effects on substrate utilization and gene expression that differed from those observed in mouse cells.

What analytical approaches can determine the specific components of HDL responsible for observed biological effects?

Determining which specific components of HDL particles are responsible for their diverse biological effects requires sophisticated analytical approaches:

• HDL fractionation and reconstitution:

  • Separation of HDL into lipid and protein components

  • Selective removal of specific proteins or lipids

  • Reconstitution of synthetic HDL with defined composition

  • Study examined apoA-I and discoidal reconstituted HDL particles alongside native HDL subclasses

• Molecular manipulation approaches:

  • Site-directed mutagenesis of key HDL proteins

  • Chemical modification of specific functional groups

  • Antibody-mediated neutralization of specific components

  • Competition experiments with purified components

• Correlation analyses:

  • Multivariate analysis correlating HDL composition with function

  • Principal component analysis to identify key determinants

  • Machine learning approaches to predict functionality from composition

  • Network analysis of HDL component interactions

• Advanced analytical techniques:

  • Hydrogen-deuterium exchange mass spectrometry for structural analysis

  • Cross-linking mass spectrometry for protein-protein interactions

  • Native mass spectrometry for intact HDL analysis

  • Molecular dynamics simulations of HDL particles

• Experimental design considerations:

  • Systematic testing of isolated components

  • Dose-response relationships

  • Time-course experiments

  • Multiple functional readouts

These approaches can help identify the specific proteins, lipids, or microRNAs within HDL particles that mediate their effects on different cellular processes, potentially leading to the development of targeted therapeutic approaches focusing on the most beneficial components of HDL.

How can HDL research methodologies be standardized to improve cross-study comparability?

The lack of standardization in HDL research methodologies represents a significant barrier to progress in the field. Several approaches can improve cross-study comparability:

• Standardized HDL isolation protocols:

  • Consensus methods for ultracentrifugation

  • Standard operating procedures for other isolation techniques

  • Reporting guidelines for isolation methods

  • Reference HDL preparations for quality control

• Functional assay standardization:

  • Validated cell lines and culture conditions

  • Reference materials for calibration

  • Interlaboratory proficiency testing

  • Detailed protocol sharing through repositories

• Reporting standards:

  • Minimum Information for HDL Functionality Studies (MIHFS)

  • Detailed methods sections with key parameters

  • Data availability in standardized formats

  • Reporting of negative and null results

• Biological reference materials:

  • Characterized HDL pools for quality control

  • Synthetic HDL standards

  • Control samples for functional assays

  • Validated positive and negative controls

• Collaborative research initiatives:

  • Multi-center validation studies

  • Pooled analysis of standardized measures

  • Precompetitive collaborations on methodology

  • International working groups on standardization

Standardization efforts would facilitate meta-analyses, improve reproducibility, and accelerate translation of HDL research findings to clinical applications. The field would benefit from consensus conferences specifically addressing methodological standardization.

What novel intervention strategies show promise for enhancing HDL functionality?

Research is increasingly focused on enhancing HDL functionality rather than simply raising HDL-C levels. Several promising intervention strategies emerge from the literature:

• Antioxidant approaches:

  • Selenium supplementation significantly increased paraoxonase 1 (PON1) activity and protein levels in hypercholesterolemic rats

  • This effect was accompanied by reduced reactive oxygen species (ROS) levels

  • Other antioxidants may have similar effects on HDL-associated enzymes

• Targeted nutritional interventions:

  • Mediterranean diet components

  • Specific fatty acid profiles

  • Polyphenol-rich foods

  • Plant sterols and stanols

• Advanced pharmacological approaches:

  • ApoA-I synthesis upregulators

  • Reconstituted HDL infusions

  • HDL mimetic peptides

  • Compounds targeting specific HDL-associated enzymes

• HDL subclass-specific interventions:

  • Strategies targeting specific HDL subpopulations with particular functionality

  • Methods to shift HDL subclass distribution toward more functional particles

• Combination approaches:

  • Multi-component lifestyle interventions

  • Complementary pharmacological strategies

  • Personalized interventions based on individual HDL profiles

• Emerging biotechnology approaches:

  • mRNA-based therapies targeting HDL metabolism

  • Gene editing approaches

  • Cell-based therapies to enhance HDL production

  • Engineered nanoparticles mimicking HDL functions

The selenium supplementation study provides a model for testing such interventions, demonstrating how a nutritional approach can enhance specific aspects of HDL functionality through increased activity of HDL-associated enzymes, independent of changes in HDL-C levels.

How might systems biology approaches advance understanding of HDL's diverse biological roles?

Systems biology approaches offer powerful tools to integrate diverse data types and uncover new insights into HDL's complex biological roles:

• Multi-omics integration:

  • Combining proteomics, lipidomics, transcriptomics, and metabolomics data

  • Integration of HDL composition with functionality measures

  • Correlation with clinical outcomes and biomarkers

  • Identification of HDL signatures associated with specific diseases

• Network analysis:

  • Protein-protein interaction networks within HDL

  • Signaling networks activated by HDL in target tissues

  • Metabolic networks affected by HDL (as suggested by the metabolic effects in )

  • Disease-specific HDL interaction networks

• Computational modeling:

  • Dynamic models of HDL metabolism and remodeling

  • Agent-based models of HDL-cell interactions

  • Pharmacokinetic/pharmacodynamic models of HDL-targeted interventions

  • Machine learning prediction of HDL functionality from composition

• Experimental design for systems approaches:

  • Time-course experiments capturing dynamic responses

  • Perturbation studies with multiple readouts

  • Multi-tissue analysis of HDL effects

  • Integrated analysis of data from diverse experimental systems

• Data management considerations:

  • Standardized data collection and annotation

  • Public repositories for HDL functional data

  • Data visualization tools for complex HDL datasets

  • Collaborative computational platforms

Systems biology approaches could help reconcile apparently contradictory findings in HDL research, such as the U-shaped relationship between HDL-C and mortality or the species-specific effects of HDL on energy metabolism , by revealing the underlying networks and feedback mechanisms that govern HDL biology in health and disease.