Evidence map›Paper›PMID 40689090›Full record

ArticleThe Lancet regional health. Southeast Asia2025

Identification of protein targets for dyslipidaemia and cardiovascular diseases among people with South Asian ancestry: a mendelian randomisation study.

Siwei Wu, Devendra Meena, Alexander Smith, Jingxian Huang, Georg W Otto, Yi-Hsuan Ko, James Yarmolinsky, Dipender Gill, Anand Rohatgi, Abbas Dehghan and 1 more

Abstract read
In one paragraph

Article in The Lancet regional health. Southeast Asia, 2025. 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. Article
  2. Article
  3. Patterns of lipid profile and genetic variations in South Asians.Cardiovascular diabetology. Endocrinology reports · 2025
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Siwei WuDepartment of Clinical Nutrition, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, China.
Devendra MeenaDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Alexander SmithDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Jingxian HuangDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Georg W OttoDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Yi-Hsuan KoDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
James YarmolinskyDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Dipender GillDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Anand RohatgiDepartment of Medicine, Division of Cardiology University of Texas Southwestern Medical Center Dallas TX, USA.
Abbas DehghanDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Ioanna TzoulakiDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.

Funding

Lipoprotein Metabolism and Excess Cardiometabolic Risk in South AsiansR01HL162300 · NHLBI · UT SOUTHWESTERN MEDICAL CENTER · PI Anand Kumar Rohatgi · 2022 to 2026
$2.9M
NHLBI NIH HHS R01 HL162300
6 · The paper itself

Abstract

Background: South Asians are considered to be at higher risk of dyslipidaemia, a modifiable risk factor for cardiovascular diseases (CVDs). We aimed to identify protein targets for dyslipidaemia and CVDs among people with South Asian ancestry. Methods: We used a two-sample mendelian randomisation (MR) approach, supplemented with MR-Egger, weighted median, colocalisation, and generalised MR (GMR), to evaluate the effect of 2800 plasma proteins on high/low/non-high-density lipoprotein cholesterol (HDL-C/LDL-C/non-HDL-C), total cholesterol, and triglycerides. Observational analyses were conducted on MR findings with strong colocalisation (posterior probability ≥ 80%) and GMR evidence. Univariate MR assessed lipid-associated proteins' effect on CVDs. Finally, we compared the effects of plasma proteins on lipids between South Asian and European populations. Findings: We identified 29 genetically proxied proteins potentially causal to at least one lipid measure, 12 of which showed strong colocalisation and GMR evidence, including angiopoietin-related protein 3 (ANGPTL3), proprotein convertase subtilisin/kexin type 9 (PCSK9), and cadherin EGF LAG seven-pass G-type receptor 2 (CELSR2). Notably, PCSK9 demonstrated a stronger association with LDL-C in Europeans compared to South Asians (βEuropean = 0.37; 95% CI 0.36, 0.38, βSouth Asian = 0.16; 95% CI 0.11, 0.21). Observational analysis suggested statistically significant interaction between PCSK9 levels with LDL-C levels in South Asians with South Asians having a significantly lower effect compared to other ethnicities (PCSK9∗South Asian; β = -0.14; 95% CI -0.174, -0.107). Additionally, we showed that CELSR2 is also linked with coronary artery disease in South Asians. Interpretation: Our study highlighted potential causal links between plasma proteins, dyslipidaemia, and CVDs in South Asians and highlighted protein targets, including CELSR2, PCSK9, ANGPTL3, and Apolipoprotein(a) (LPA). Notably, our study indicated that PCSK9 has a significantly weaker effect on LDL-C in South Asians than Europeans. Funding: This work is supported by the British Heart Foundation Research Excellence Award (4) (RE/24/130023). IT and AR are supported by NIHR01 HL162300-02.

Indexed as

Asian ancestryCardiovascular disease riskDyslipidemiaMendelian randomizationProteomicsSouth Asian ancestrySouth Asians

Identifiers

PMID40689090
PMCPMC12271078

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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.