{"id":1144,"date":"2024-09-29T14:20:58","date_gmt":"2024-09-29T14:20:58","guid":{"rendered":"https:\/\/aihealthinsight.org\/?p=1144"},"modified":"2024-10-01T13:03:39","modified_gmt":"2024-10-01T13:03:39","slug":"1144-2","status":"publish","type":"post","link":"https:\/\/aihealthinsight.org\/1144-2\/","title":{"rendered":"Another Post on V-LDL and LDL-C"},"content":{"rendered":"<h3 class=\"wp-block-heading\">J Am Heart Assoc. 2024;13:e031878. DOI: 10.1161\/JAHA.123.031878<\/h3><h3 class=\"wp-block-heading\">SUMMARY<\/h3><p>This study investigates the impact of discordance between very low-density lipoprotein cholesterol (VLDL-C) and low-density lipoprotein cholesterol (LDL-C) on cardiovascular disease (ASCVD) risk.<\/p><h3 class=\"wp-block-heading\">AUTHORS<\/h3><ul class=\"wp-block-list\"><li>Kristina E. Seehusen, MPH<\/li>\n\n<li>Alan T. Remaley, MD, PhD<\/li>\n\n<li>Maureen Sampson, BS<\/li>\n\n<li>Jeffrey W. Meeusen, PhD<\/li>\n\n<li>Nicholas B. Larson, PhD<\/li>\n\n<li>Paul A. Decker, MS<\/li>\n\n<li>Jill M. Killian, BS<\/li>\n\n<li>Paul Y. Takahashi, MD, MPH<\/li>\n\n<li>V\u00e9ronique L. Roger, MD, MPH<\/li>\n\n<li>Sheila M. Manemann, MPH<\/li>\n\n<li>Reyna Lam, BS<\/li>\n\n<li>Suzette J. Bielinski, PhD<\/li><\/ul><h3 class=\"wp-block-heading\">AUTHOR ORGANIZATIONS<\/h3><ul class=\"wp-block-list\"><li>University of Minnesota School of Public Health<\/li>\n\n<li>Mayo Clinic, Rochester<\/li>\n\n<li>National Institutes of Health (NIH), Bethesda<\/li>\n\n<li>Rochester Epidemiology Project<\/li><\/ul><h3 class=\"wp-block-heading\">FINDINGS<\/h3><ul class=\"wp-block-list\"><li>VLDL-C is more strongly linked to ASCVD than LDL-C.<\/li>\n\n<li>Discordantly high VLDL-C and low LDL-C increase ASCVD risk significantly.<\/li>\n\n<li>Lipid discordance suggests possible residual ASCVD risk despite normal LDL-C levels.<\/li><\/ul><h3 class=\"wp-block-heading\">STUDY DETAILS<\/h3><ul class=\"wp-block-list\"><li>Large geographically defined cohort of nearly 40,000 individuals.<\/li>\n\n<li>12-year follow-up using clinical lipid panels.<\/li>\n\n<li>Use of the Sampson equation to calculate lipid levels.<\/li>\n\n<li>Examined ASCVD outcomes like stroke, myocardial infarction, and coronary interventions.<\/li>\n\n<li>Concordance and discordance of LDL-C and VLDL-C levels assessed.<\/li><\/ul><h3 class=\"wp-block-heading\">STUDY QUALITY<\/h3><p><strong>STUDY DESIGN<\/strong><br>A large cohort study using clinically derived lipid panel data for longitudinal analysis.<\/p><p><strong>SAMPLE SIZE<\/strong><br>Nearly 40,000 participants with a median follow-up of 12 years.<\/p><p><strong>CONFIDENCE INTERVALS<\/strong><br>Risk estimates reported with 95% confidence intervals across LDL-C and VLDL-C.<\/p><p><strong>P-VALUE<\/strong><br>Results show significant p-values for many associations, particularly VLDL-C and ASCVD risk (p &lt; 0.001).<\/p><p><strong>EFFECT SIZE<\/strong><br>VLDL-C had a strong hazard ratio of 1.07 for ASCVD risk per 10 mg\/dL increase.<\/p><p><strong>CONSISTENCE OF RESULTS<\/strong><br>Consistent results across discordant\/concordant lipid pairings and across models.<\/p><p><strong>METHODOLOGY TRANSPARENCY<\/strong><br>Methods, such as the use of the Sampson equation, are well documented and replicable.<\/p><p><strong>STUDY REPRODUCIBILITY<\/strong><br>The study used established methodologies and publicly accessible data for reproducibility.<\/p><p><strong>DATA ANALYSIS METHOD<\/strong><br>Cox proportional hazards regression and generalized additive models used for association analysis.<\/p><h3 class=\"wp-block-heading\">CONFLICTS OF INTEREST<\/h3><p>None detected.<\/p><h3 class=\"wp-block-heading\">RESEARCHER&#8217;S INTERPRETATION<\/h3><p>VLDL-C discordance with LDL-C reveals a significant, underrecognized ASCVD risk, requiring enhanced clinical assessments.<\/p><h3 class=\"wp-block-heading\">PAPER QUALITY<\/h3><p><strong>Novelty<\/strong>: 8\/10<br>The focus on VLDL-C discordance offers new insights into residual cardiovascular risk.<\/p><p><strong>Rigor<\/strong>: 9\/10<br>Strong methodology, large sample size, and long-term follow-up provide robust evidence.<\/p><p><strong>Empiricism<\/strong>: 8\/10<br>Empirical evidence was collected over a long period, focusing on clinical outcomes and lipid measurements.<\/p><h3 class=\"wp-block-heading\">RATING CHART<\/h3><p>Known [&#8212;&#8211;8&#8212;-] Novel<br>Weak [&#8212;&#8212;&#8211;9-] Rigorous<br>Theoretical [&#8212;&#8211;8&#8212;-] Empirical<\/p><h3 class=\"wp-block-heading\">FINAL SCORE<\/h3><p>A<br>This paper demonstrates novel, rigorous findings, with no conflicts of interest, and provides clear empirical evidence of ASCVD risk associated with VLDL-C.<\/p><h3 class=\"wp-block-heading\">SUMMARY STATEMENT<\/h3><p>VLDL-C discordance increases ASCVD risk, suggesting the need for improved risk assessments in patients with discordant lipid levels, even if LDL-C appears normal.<\/p>","protected":false},"excerpt":{"rendered":"<p>J Am Heart Assoc. 2024;13:e031878. DOI: 10.1161\/JAHA.123.031878 SUMMARY This study investigates the impact of discordance between very low-density lipoprotein cholesterol (VLDL-C) and low-density lipoprotein cholesterol (LDL-C) on cardiovascular disease (ASCVD)&hellip;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_vp_format_video_url":"","_vp_image_focal_point":[],"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1144","post","type-post","status-publish","format-standard","hentry","category-2024-upgrade"],"_links":{"self":[{"href":"https:\/\/aihealthinsight.org\/wp-json\/wp\/v2\/posts\/1144","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aihealthinsight.org\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aihealthinsight.org\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aihealthinsight.org\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/aihealthinsight.org\/wp-json\/wp\/v2\/comments?post=1144"}],"version-history":[{"count":5,"href":"https:\/\/aihealthinsight.org\/wp-json\/wp\/v2\/posts\/1144\/revisions"}],"predecessor-version":[{"id":1152,"href":"https:\/\/aihealthinsight.org\/wp-json\/wp\/v2\/posts\/1144\/revisions\/1152"}],"wp:attachment":[{"href":"https:\/\/aihealthinsight.org\/wp-json\/wp\/v2\/media?parent=1144"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aihealthinsight.org\/wp-json\/wp\/v2\/categories?post=1144"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aihealthinsight.org\/wp-json\/wp\/v2\/tags?post=1144"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}