2025 年 2 月，南方醫(yī)科大學(xué)南方醫(yī)院在腦血管病領(lǐng)域權(quán)威期刊《Stroke》雜志第 2 期發(fā)表了題為《Large-Scale Plasma Proteomics Profiles for Predicting Ischemic Stroke Risk in the General Population》（大規(guī)模血漿蛋白質(zhì)組學(xué)分析用于預(yù)測(cè)一般人群的缺血性腦卒中風(fēng)險(xiǎn)）的研究論文。此研究憑借其創(chuàng)新性與顯著的臨床價(jià)值，被《Stroke Alert Podcast》節(jié)目評(píng)選為當(dāng)期亮點(diǎn)文章，并進(jìn)行了深度專題報(bào)道。

在《Stroke Alert Podcast》第 49 期節(jié)目中，主持人 Negar Asdaghi 博士對(duì)該論文進(jìn)行了重點(diǎn)推介。

On Episode 49 of the Stroke Alert Podcast, host Dr. Negar Asdaghi highlights an article from the February 2025 issue of Stroke:“Large-Scale Plasma Proteomics Profiles for Predicting Ischemic Stroke Risk in the General Population.”

Negar Asdaghi 博士：我們先從這些問題開始。
Dr. Negar Asdaghi: Let's start with some questions.

1) 我們能否通過檢測(cè)某些血漿蛋白來預(yù)測(cè)后續(xù)患缺血性中風(fēng)的風(fēng)險(xiǎn)呢？

1) Can certain plasma proteins predict the future risk of ischemic stroke?

2) 如果通過血液檢測(cè)能在缺血性中風(fēng)癥狀出現(xiàn)之前，就判斷出是否有患病風(fēng)險(xiǎn)，這會(huì)帶來怎樣的影響呢？
2) What if your blood could reveal whether you're at risk for an ischemic stroke long before symptoms appear?

在本月的期刊里，來自中國(guó)廣州南方醫(yī)科大學(xué)南方醫(yī)院國(guó)家腎臟病臨床醫(yī)學(xué)研究中心的秦獻(xiàn)輝教授、甘小琴博士及他們的同事，在一篇題為《大規(guī)模血漿蛋白質(zhì)組學(xué)分析用于預(yù)測(cè)一般人群的缺血性腦卒中風(fēng)險(xiǎn)》的文章中，探討了缺血性腦卒中風(fēng)險(xiǎn)和血漿蛋白質(zhì)組學(xué)之間的有趣聯(lián)系。下面，讓我們一起來深入了解一下，看看這些研究者是如何把血液變成預(yù)測(cè)未來的 “水晶球” 的。為了驗(yàn)證這個(gè)想法，研究者們采用了英國(guó)生物銀行的數(shù)據(jù)。經(jīng)常收聽《Stroke Alert》播客的朋友，應(yīng)該對(duì)英國(guó)生物銀行不陌生。在我們的播客系列中，已經(jīng)介紹過好幾項(xiàng)利用該生物銀行數(shù)據(jù)開展的研究。

In this month's issue of the journal, Dr. Xiaoqin Gan from National Clinical Research Center for Kidney Disease at Southern Medical University at Guangzhou, China, and colleagues explore a fascinating connection between the risk of ischemic stroke and the world of plasma proteomics in an article titled "Large-Scale Plasma Proteomics Profiles for Predicting Ischemic Stroke Risk in the General Population." Let's dive in and see how the authors are turning blood into a crystal ball. To explore this idea, the authors use data from the UK Biobank. Our Stroke Alert Podcast listeners are now well aware of the UK Biobank. We've covered several studies in our podcast series that have used data from this biobank.

我們知道，英國(guó)生物銀行是一個(gè)大規(guī)模的醫(yī)學(xué)數(shù)據(jù)庫(kù)，包含了近50萬名年齡在40至70歲之間的參與者的去識(shí)別化健康信息。其最大的優(yōu)勢(shì)在于，不僅是海量醫(yī)學(xué)數(shù)據(jù)的集合，更是一個(gè)對(duì)所有研究人員開放的共享資源平臺(tái)，為全球醫(yī)學(xué)科研發(fā)展提供了有力支撐。

We know that the UK Biobank is a large-scale medical database containing de-identified health information from close to half a million participants that are between the ages of 40 and 70. The very important point about the UK Biobank is not only a great research resource, but it's also available to any researcher to use.

讓我們聚焦本期期刊中的這篇蛋白質(zhì)組學(xué)研究論文。在這項(xiàng)研究里，作者們用到了英國(guó)生物銀行的一個(gè)子集，叫 “UK Biobank Pharma Proteomics Project，簡(jiǎn)稱 UKB - PPP” 。這個(gè)項(xiàng)目涵蓋了超過 53,000 名參與者，他們?cè)诩尤胗?guó)生物銀行時(shí)，都接受了蛋白質(zhì)組學(xué)檢測(cè)。在這篇論文中，作者僅保留了英國(guó)白人血統(tǒng)的個(gè)體，排除了有缺血性腦卒中史或心房顫動(dòng)史的患者，最終納入 43,000 多例患者用于分析。

So, let's go back to our proteomic paper in this issue of the journal. For this study, the authors use a subset of the UK Biobank known as the Pharma Proteomics Project. This project included over 53,000 participants who underwent proteomic measurement testing as part of their enrollment in the UK Biobank. For this paper, when they applied various exclusion criteria for inclusion in this study, importantly only keeping individuals of British White descent and excluding patients with a prior history of ischemic stroke or history of atrial fibrillation, they were left with over 43,000 patients for this analysis.

那么，研究團(tuán)隊(duì)具體開展了哪些研究工作呢？首先，他們依托強(qiáng)大的蛋白質(zhì)組學(xué)數(shù)據(jù)庫(kù)，從近 3000 種已測(cè)量的血漿蛋白中，運(yùn)用 Lasso Cox 回歸分析精準(zhǔn)識(shí)別出 17 種與缺血性腦卒中密切相關(guān)的蛋白質(zhì)，并以此為基礎(chǔ)創(chuàng)建了一個(gè)蛋白質(zhì)風(fēng)險(xiǎn)評(píng)分體系。在這 17 種蛋白質(zhì)中，部分蛋白質(zhì)與未來發(fā)生缺血性腦卒中的風(fēng)險(xiǎn)呈現(xiàn)正相關(guān)關(guān)系，也就是說，這些蛋白質(zhì)可能會(huì)導(dǎo)致缺血性腦卒中風(fēng)險(xiǎn)增加；而另一部分蛋白質(zhì)則呈現(xiàn)負(fù)相關(guān)關(guān)系，意味著它們?cè)谝欢ǔ潭壬夏軌蚱鸬奖Ｗo(hù)作用，降低缺血性腦卒中發(fā)生的可能性。為了評(píng)估這個(gè)蛋白質(zhì)風(fēng)險(xiǎn)評(píng)分體系的預(yù)測(cè)性能，研究人員引入了 C 統(tǒng)計(jì)量這一關(guān)鍵指標(biāo)。C 統(tǒng)計(jì)量是衡量預(yù)測(cè)模型準(zhǔn)確性的重要標(biāo)準(zhǔn)，進(jìn)一步分析顯示，這個(gè)新構(gòu)建的蛋白質(zhì)風(fēng)險(xiǎn)評(píng)分體系在預(yù)測(cè)缺血性腦卒中時(shí)，C 統(tǒng)計(jì)量達(dá)到了 0.76，這表明該評(píng)分體系具有較高的預(yù)測(cè)價(jià)值。值得注意的是，與傳統(tǒng)的僅基于經(jīng)典血管風(fēng)險(xiǎn)因素（如高血壓、高血脂、高血糖等）、年齡和其他臨床特征得出的缺血性腦卒中臨床風(fēng)險(xiǎn)評(píng)分相比，新的蛋白質(zhì)風(fēng)險(xiǎn)評(píng)分體系的 C 統(tǒng)計(jì)量更高；同時(shí)，也超過了英國(guó)生物銀行此前發(fā)布的針對(duì)未來缺血性腦卒中風(fēng)險(xiǎn)的多基因風(fēng)險(xiǎn)評(píng)分。這充分表明，新構(gòu)建的蛋白質(zhì)風(fēng)險(xiǎn)評(píng)分體系在預(yù)測(cè)缺血性腦卒中風(fēng)險(xiǎn)方面，具有更為顯著的優(yōu)勢(shì)，有望為臨床預(yù)防和早期診斷提供更有力的支持。

So, what did they do? First off, using the proteomic data bank, they identified 17 proteins out of close to 3,000 measured plasma proteins to create a protein risk score. Some of these proteins were positively associated with a higher future risk of development of ischemic stroke, and some had a negative association. In other words, some were protective and some were, in a sense, a risk for future stroke. So, using Lasso Cox regression, they came up with the protein risk score, and it turns out that the protein risk score had a pretty decent C statistic. In other words, a pretty decent predictive value for predicting the odds of future ischemic stroke. The C score was 0.76. This was certainly higher than the ischemic stroke clinical risk score that uses classic vascular risk factors and age and other characteristics, or the polygenic risk score that had previously been released by the UK Biobank on the same outcome, which is the future risk of development of ischemic stroke.

更重要的是，在這 17 種蛋白質(zhì)里，有 5 種在回歸分析具有最高的絕對(duì)系數(shù)，表明這 5 種蛋白質(zhì)在預(yù)測(cè)缺血性腦卒中方面發(fā)揮著最為關(guān)鍵的作用，貢獻(xiàn)度最強(qiáng)。令人興奮的是，研究人員僅憑借這 5 種蛋白質(zhì)，再融合參與者的年齡和性別這些基礎(chǔ)信息，就成功構(gòu)建出一個(gè)更為簡(jiǎn)易的蛋白質(zhì)組預(yù)測(cè)風(fēng)險(xiǎn)評(píng)分。經(jīng)測(cè)試，這個(gè)新評(píng)分與最初復(fù)雜的包括17種蛋白質(zhì)的風(fēng)險(xiǎn)評(píng)分相比，C 統(tǒng)計(jì)量近乎一致，預(yù)測(cè)能力也相差無幾。這意味著復(fù)雜的蛋白質(zhì)組學(xué)風(fēng)險(xiǎn)評(píng)分（需要更高的檢測(cè)能力來測(cè)量多種血漿蛋白）可以被一個(gè)更簡(jiǎn)單的評(píng)分所取代。新的評(píng)分僅需測(cè)量五種血漿蛋白，再加上參與者的年齡和性別，便能在預(yù)測(cè)參與者未來缺血性腦卒中風(fēng)險(xiǎn)方面，展現(xiàn)出與復(fù)雜評(píng)分極為相似的價(jià)值。這一發(fā)現(xiàn)極具意義，為后續(xù)的臨床應(yīng)用開辟了新路徑，帶來了更多便利與可能，有望在缺血性腦卒中的預(yù)防和診療領(lǐng)域發(fā)揮重要作用。

Now, this is exciting. Better yet, they found that out of these 17 proteins, five had the most predictive value given that they had the highest absolute coefficient in the regression analysis. So, what made it really nice was that taking these five proteins alone and adding just simply age and sex of the participants, they were able to create a simpler proteomic predictive risk score that had essentially the same C statistics or predictive power as the original proteomic risk score. In essence, that would mean that the complex proteomic risk score, which would require higher capabilities to measure various plasma proteins, can be replaced by a much simpler score with only five plasma protein measurements and additions of participants' age and sex, to have very similar values in terms of predicting the future risk of ischemic stroke in any participant. Very interesting.

綜上所述，這項(xiàng)研究成果表明，在不久的將來，我們或許只需進(jìn)行一次快速的血液檢測(cè)，再結(jié)合一些簡(jiǎn)單的計(jì)算，就能精準(zhǔn)預(yù)測(cè)出哪些人群存在較高的缺血性腦卒中發(fā)病風(fēng)險(xiǎn)。這項(xiàng)研究充分地展現(xiàn)了蛋白質(zhì)組學(xué)技術(shù)的巨大潛力，使我們朝著個(gè)性化醫(yī)學(xué)和精準(zhǔn)風(fēng)險(xiǎn)評(píng)估的目標(biāo)又邁進(jìn)了一大步。通過對(duì)特定蛋白質(zhì)的分析，我們能夠更深入地了解個(gè)體的健康狀況，提前識(shí)別潛在的風(fēng)險(xiǎn)因素。這意味著，在缺血性腦卒中發(fā)生之前，我們就有足夠的時(shí)間和能力制定并實(shí)施有效的預(yù)防措施，從而降低疾病的發(fā)生率，提高患者的生活質(zhì)量。相信隨著蛋白質(zhì)組學(xué)技術(shù)的不斷發(fā)展和完善，它將在臨床實(shí)踐中發(fā)揮更為重要的作用，為人類的健康事業(yè)做出更大的貢獻(xiàn)。

So, in the future, we may be able to do a quick blood test and some simple calculation, and predict with a lot more certainty who will or who will not develop an ischemic stroke. This study is an example of how proteomic technology can get us closer to personalized medicine and risk ascertainment, and perhaps the ability to do corrective measures way before an incident ischemic stroke occurs.

參考文獻(xiàn)：
Gan X, Yang S, Zhang Y, Ye Z, Zhang Y, Xiang H, Huang Y, Wu Y, Zhang Y, Qin X. Large-Scale Plasma Proteomics Profiles for Predicting Ischemic Stroke Risk in the General Population.Stroke. 2025 Feb;56(2):456-464. doi: 10.1161/STROKEAHA.124.048654.

Stroke Alert Podcast網(wǎng)頁鏈接:
https://www.ahajournals.org/do/10.1161/podcast.20250212.411369

編輯| 甘小琴 蔡湘連
審核| 秦獻(xiàn)輝 張園園