Evidence map›Paper›PMID 39334296›Full record

ArticleLipids in health and disease2024

Estimating the effect of inclisiran on hypercholesterolemia and primary prevention of cardiovascular disease: the NHANES 1999-2018 study.

Haonan Li, Song Zhao, Jiawen Wu, Jun Han, Yawei Xu, Shengfeng Shi, Yi Zhang

Abstract read
In one paragraph

Article in Lipids in health and disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Features of Lipid Disorders in Cardiovascular-Kidney-Metabolic Syndrome.International journal of molecular sciences · 2026
    Review
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Haonan Li *Department of Cardiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, 301 Middle Yanchang Road, Shanghai, 200072, China.
Song Zhao *Department of Cardiology, Guizhou Provincial People's Hospital, 83 Zhongshan East Road, Guiyang, Guizhou, 550002, China.
Jiawen WuDepartment of Cardiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, 301 Middle Yanchang Road, Shanghai, 200072, China.
Jun HanDepartment of Cardiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, 301 Middle Yanchang Road, Shanghai, 200072, China.
Yawei XuDepartment of Cardiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, 301 Middle Yanchang Road, Shanghai, 200072, China.
Shengfeng ShiDepartment of Cardiology, Nantong Haimen People's Hospital, 1201 Beijing West Road, Nantong, Jiangsu, 226199, China. hmssf@sohu.com.
Yi ZhangDepartment of Cardiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, 301 Middle Yanchang Road, Shanghai, 200072, China. yizshcn@gmail.com.

Funding

Grant for the construction of Innovative Flagship Hospital for Integrated Traditional Chinese and Western Medicine ZY(2021-2023)-0205-05National Nature Science Foundation of China 82170388Shanghai Technology Research Leader Program 21XD1434700the Cardiac rehabilitation fund by the International Medical Exchange Foundation Z-2019-42-1908-3
6 · The paper itself

Abstract

backgroundHypercholesterolemia has been identified as an independent predictor of cardiovascular disease (CVD). Inclisiran, an innovative small interfering RNA agent, is anticipated to result in a notable reduction of approximately 50% in low-density lipoprotein cholesterol (LDL-C) levels. Given its transformative impact, this study scrutinized the eligibility of the US population for inclisiran treatment and evaluated its potential effects on hypercholesterolemia and the primary prevention of CVD.

methodsThis study applied the eligibility criteria from the ORION 10 and 11 trials to the 1999-2018 National Health and Nutrition Examination Survey (NHANES) dataset to estimate the size of the eligible population for atherosclerotic cardiovascular disease (ASCVD) and ASCVD-risk equivalents. Utilizing the reduction in LDL-C levels from ORION 10, this study predicted the impact of inclisiran on LDL-C levels among ASCVD patients. Similarly, leveraging the changes in lipid levels from ORION 11, this study predicted inclisiran's effect on the 10-year change in CVD risk and preventable CVD events in the ASCVD-risk equivalents population, employing the Framingham CVD Risk Score.

resultsThe study identified 579 ASCVD patients (5 million) and 382 ASCVD-risk equivalents (2.66 million) who met the eligibility criteria from ORION 10 and 11. Among the ASCVD population, 3.5 million (70.2%) would achieve a ≥ 50% reduction in LDL-C levels after treatment. Furthermore, 4.6 million (91.3%) would achieve LDL-C levels < 70 mg/dL, and 3.8 million (75%) would achieve LDL-C levels < 55 mg/dL after treatment. For the ASCVD-risk equivalents population, the estimated 10-year CVD risk would decrease from 25.3 to 17.7%, an absolute reduction of 7.6% and a relative reduction of 30% following inclisiran treatment, potentially preventing 202,353 CVD events over a decade, including 138,084 coronary heart disease cases, 37,351 strokes, and 23,894 congestive heart failure cases.

conclusionsInclisiran has the potential to substantially reduce the prevalence of hypercholesterolemia and prevent nearly 200,000 CVD events in eligible US adults.

Indexed as

Cardiovascular DiseasesCholesterol, LDLHypercholesterolemiaNutrition SurveysPrimary PreventionAdultAgedFemaleHumansMaleMiddle AgedOligonucleotidesRNA, Small InterferingALN-PCSCholesterol, LDLOligonucleotidesRNA, Small InterferingCardiovascular diseaseHypercholesterolemiaInclisiranLDL-CNHANES

Identifiers

PMID39334296
PMCPMC11430533

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.