点击“蓝字”关注我们
泰达国际心血管病医院 郑 刚
心-肾-代谢综合征(CKM)是去年美国心脏协会(AHA)为应对代谢和肾脏疾病的高发而提出的一种新概念。流行病学数据表明,随着个体从CKM 0期进展到3期,动脉粥样硬化性心血管疾病(ASCVD)和心力衰竭的绝对风险较高,但需要重新制定风险评估的最佳策略。绝对风险评估的目标是将干预措施的类型、强度与预测的风险和预期治疗效果相匹配,这始终是一级预防的核心所在。随着医疗技术的进步,能够同时解决CKM不同发生机制的方法越来越多,需要新的风险预测模型,其中包含与CKM背景相关的预测因素和预后结果,同时还应考虑健康的社会决定因素。这些因素是心血管疾病的关键上游驱动因素,有助于更公平地估计和应对疾病风险。
一、CKM定义
CKM综合征定义为一组由代谢异常、慢性肾病(CKD)和心血管疾病(CVD)之间病理生理相互作用导致的全身性疾病[1]。这种综合征反映了代谢危险因素、CKD和心血管系统之间的相互作用,并对发病率和死亡率有深远影响。
二、CVD的既定危险因素
CVD的大部分风险可归因于传统风险因素,即使在亚临床水平升高的情况下也是如此(如血压升高而不符合高血压标准)[2-5]。在一项对3项大型前瞻性研究的分析中,几乎所有经历过非致死性冠心病事件的人(92%的男性和87%的女性)在事件发生前都至少有一个主要危险因素(如总胆固醇升高[≥6.22 mmol/L或≥240 mg/dl]、收缩压≥140 mm Hg、舒张压≥90 mm Hg、吸烟或糖尿病)。对于致命的冠心病事件,也观察到了类似情况[6]。在VIRGO研究中,一项由18~55岁患有早发性心肌梗死的个体组成的前瞻性观察队列中,传统危险因素的人群归因分数为85%。这些研究和其他研究(如INTERHEART62)强调了传统风险因素对CVD风险评估的主要贡献[2],因此有必要将其纳入更新的风险方程中。
在条件允许的情况下,将风险因素水平建模为连续预测因子,有助于识别那些在多种风险因素(例如处于高血压前期范围的血压、糖尿病前期范围的血糖)上呈现亚临床升高的个体。这些个体即便尚未达到基于阈值定义的风险状态(如高血压、糖尿病),其风险水平也相对较高。此外,传统的风险因素也在临床实践中进行常规测量,是预防性治疗的目标,从而在风险评估和治疗干预之间建立了一致性。尽管年龄和性别不可改变,但它们都是CVD风险的关键组成部分,也是CVD风险方程中的重要预测因素。健康行为,包括身体活动和饮食质量,对于降低心血管疾病风险至关重要[7]。
以往这些因素未被纳入风险预测模型,是因为它们所带来的风险大多已通过模型中已包含的其他CVD风险因素(如高血压、糖尿病)得到了体现[8,9]。另外,低水平或不健康的心肺功能(CRF)作为衡量心脏代谢健康的一个综合指标,与成年人CVD风险及全因死亡率的增加密切相关[10]。
三、CVD和CKM健康指标的新风险标志物
界内一直在寻求超越传统CVD风险因素的评估方法,以更精准地预测心血管疾病风险,并不断探索可能进一步增强这一评估的新型CVD风险标志物。流行病学研究已经证实,CKM风险标志物(如肾功能、代谢健康状况)与总体CVD、特定CVD亚型、ASCVD以及心力衰竭(HF)之间存在紧密关联[11-15]。长期研究不断涌现,为这些因素与CVD、ASCVD及HF终生风险之间的联系提供了有力证据[16,17]。不仅CVD的负担在CKD患者中更为沉重,而且数据显示,肾功能不佳的人群CVD的发病年龄更早[18-20]。
CKD患者面临较高的CVD风险,这一观点已得到广泛认可,并是AHA此前科学声明的重点内容[11]。事实上,CKD患者因CVD事件死亡的可能性甚至高于其进展至肾功能衰竭的风险。CKD预后联盟的一项超过900万人的分析显示,随着eGFR(估算肾小球滤过率)每降低15 ml/(min·1.73 m2),ASCVD的风险和致命性冠心病均显著增加,且这一关联独立于其他风险因素存在[21]。因此,支持将eGFR纳入CVD风险预测,理由还包括其可以常规测量以及在实验室系统中自动计算的便捷性。
目前,建议糖尿病或CKD患者每年或根据CKD风险状态进行蛋白尿筛查,或进行尿液白蛋白与肌酐比值(UACR)检测[22,23]。此测试简便、经济,应定期重复以指导监测和治疗。有证据表明,不论是否患高血压、糖尿病和CKD,UACR高水平与CVD事件存在关联[23]。即使非糖尿病患者,低水平白蛋白尿也增加心肌梗死、卒中等风险。在CVD亚型中,UACR高负荷还与临床前HF及HF本身相关。因此,建议CKM2期及以上患者每年检测UACR,以有效管理健康和评估风险。
在代谢健康预测因素中,体重指数(BMI)虽易获取且为心血管疾病(CVD)风险因素,但其与动脉粥样硬化性CVD(ASCVD)的短期关联多由其他主要风险因素介导,故在风险预测方程中附加效用有限[24,25]。然而,忽略BMI可能导致对高BMI个体的风险评估不准。一项研究显示,预测模型虽辨别力良好,但高估了ASCVD风险,尤其对于中度或重度肥胖个体。
血糖异常与CVD风险紧密相关,无论是否患糖尿病。建议通过糖化血红蛋白(HbA1c)或空腹血糖进行筛查,频率根据CKM分期而定。纳入血糖状态连续测量可优化糖尿病患者风险预测。此外,载脂蛋白B(ApoB)水平在某些情况下比传统血脂指标更具预测价值,尤其在CKM健康状况不佳人群中[26]。
高脂蛋白(a)[Lp(a)]也是ASCVD的风险增强因素,其优点在于稳定性高。虽然目前没有明确的指导方针建议评估Lp(a)的适用人群,但如果直接靶向降低Lp(a)的新疗法证明了预防ASCVD的临床有效性和安全性,那么未来可能需要提高的Lp(a)筛查和监测率[27,28]。
考虑到心脏损伤的生物标志物(如高敏肌钙蛋白、B型利钠肽)和诊断成像(CT、超声心动图)对ASCVD和HF预测的重要性,尽管它们与CVD关联紧密,但因缺乏普及检测的建议、成本考量及检测后续影响,未被纳入PREVENT模型。B型利钠肽尤其与心力衰竭事件独立相关,可增强预测效果,并被专业指南推荐用于筛查无症状高风险个体。同时,美国糖尿病协会也建议高危糖尿病患者每年检测利钠肽[29]。
广泛筛查生物标志物仍面临成本和临床操作挑战,它们或更适合作为序贯诊断测试,评估亚临床CVD并调整患者风险分类,类似于CT测量冠状动脉钙化用于特定患者[30]。其他生物标志物如高敏C反应蛋白等,因无症状个体缺乏常规测量,未纳入当前模型。此外,尽管早发CVD家族史具有参考价值,但在多数临床环境中应用不一,且研究显示其并未显著提升模型预测能力。因此,在平衡成本效益与临床需求后,当前策略倾向于针对特定情况采用生物标志物检测,以优化CVD的风险评估与管理。
关于“OMIC”标记物(如蛋白质组学、代谢组学、基因组学)与CVD风险关联的研究激发了精准医学的热情。尽管这些前沿研究揭示了疾病机制的新进展,但现有数据不支持在大规模普通人群中使用基因组和蛋白质组评分进行风险预测。例如,将多基因风险评分与传统风险因素结合,并未显著提高中年至老年人的风险辨别能力。直接比较显示,仅冠状动脉钙化(CAC)改善了风险辨别[31,32]。未来研究应聚焦于哪些人群能从新生物标志物的序贯测试中获益,以更精准地指导CVD风险评估。
小结
随着生活方式的改变和人口老龄化加剧,CVD、CKD和代谢性疾病(如2型糖尿病等)的发病率不断攀升。这些疾病不仅单独威胁着人类的健康,更因其紧密的病理生理联系而共同作用,形成了一个全新的健康挑战——CKM综合征。CKM综合征的管理需要多学科协作,包括心血管科、肾脏科、内分泌科、营养科等。通过团队合作,为患者提供个性化的治疗方案,以改善预后和生活质量。CKM综合征作为一种全身性疾病,其管理需要临床从整体出发,综合考虑心脏、肾脏和代谢因素。通过早期筛查、健康生活方式的推广、药物治疗以及多学科协作,使患者临床获益。
郑刚 教授
泰达国际心血管病医院
现任泰达国际心血管病医院特聘专家,济兴医院副院长
中国高血压联盟理事,中国心力衰竭学会委员,中国老年医学会高血压分会天津工作组副组长,中国医疗保健国际交流促进会高血压分会委员
天津医学会心血管病专业委员会委员,天津医学会老年病专业委员会常委,天津市医师协会高血压专业委员会常委,天津市医师协会老年病专业委员会委员,天津市医师协会心力衰竭专业委员,天津市医师协会心血管内科医师分会双心专业委员会委员,天津市心脏学会理事,天津市心律学会第一届委员会委员,天津市房颤中心联盟常委,天津市医药学专家协会第一届心血管专业委员会委员,天津市药理学会临床心血管药理专业委员会常委,天津市中西医结合学会心血管疾病专业委员会常委
《中华临床医师杂志(电子版)》特邀审稿专家,《中华诊断学电子杂志》《心血管外科杂志(电子版)》审稿专家,《华夏医学》副主编,《中国心血管杂志》常务编委,《中国心血管病研究》杂志第四届编委,《中华老年心脑血管病杂志》《世界临床药物》《医学综述》《中国医药导报》《中国现代医生》编委
本人在专业期刊和心血管网发表文章979篇,其中第一作者790篇,参加著书11部。获天津市2005年度“五一劳动奖章和奖状”和“天津市卫生行业第二届人民满意的好医生”称号
参考文献
(上下滑动可查看)
1.Ndumele CE, Rangaswami J, Chow SL, et al. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association. Circulation. 2023 Oct 9.
2.Pencina MJ, Navar AM, Wojdyla D, Sanchez RJ, Khan I, Elassal J, D’Agostino RB Sr, Peterson ED, Sniderman AD. Quantifying importance of major risk factors for coronary heart disease. Circulation. 2019;139:1603–1611. doi: 10.1161/CIRCULATIONAHA.117.031855
3. Yusuf S, Joseph P, Rangarajan S, Islam S, Mente A, Hystad P, Brauer M, Kutty VR, Gupta R, Wielgosz A, et al. Modiffable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middleincome, and low-income countries (PURE): a prospective cohort study. Lancet. 2020;395:795–808. doi: 10.1016/s0140-6736(19)32008-2
4.Miura K, Daviglus ML, Dyer AR, Liu K, Garside DB, Stamler J, Greenland P. Relationship of blood pressure to 25-year mortality due to coronary heart disease, cardiovascular diseases, and all causes in young adult men: the Chicago Heart Association Detection Project in Industry. Arch Intern Med. 2001;161:1501–1508. doi: 10.1001/archinte.161.12.1501
5.Stamler J, Daviglus ML, Garside DB, Dyer AR, Greenland P, Neaton JD. Relationship of baseline serum cholesterol levels in 3 large cohorts of younger men to long-term coronary, cardiovascular, and all-cause mortality and to longevity. JAMA. 2000;284:311–318. doi: 10.1001/jama.284.3.311
6.Greenland P, Knoll MD, Stamler J, Neaton JD, Dyer AR, Garside DB, Wilson PW. Major risk factors as antecedents of fatal and nonfatal coronary heart disease events. JAMA. 2003;290:891–897. doi: 10.1001/jama.290.7.891
7. Lloyd-Jones DM, Allen NB, Anderson CA, Black T, Brewer LC, Foraker RE, Grandner MA, Lavretsky H, Perak AM, Sharma G, et al. Life’s Essential 8: updating and enhancing the American Heart Association’s construct of cardiovascular health: a presidential advisory from the American Heart Association. Circulation. 2022;146:e18–e43. doi: 10.1161/CIR.0000000000001078
8. Pandey A, Mehta A, Paluch A, Ning H, Carnethon MR, Allen NB, Michos ED, Berry JD, Lloyd-Jones DM, Wilkins JT. Performance of the American Heart Association/American College of Cardiology pooled cohort equations to estimate atherosclerotic cardiovascular disease risk by selfreported physical activity levels. JAMA Cardiol. 2021;6:690–696. doi: 10.1001/jamacardio.2021.0948
9. Jeong SY, Wee CC, Kovell LC, Plante TB, Miller ER 3rd, Appel LJ, Mukamal KJ, Juraschek SP. Effects of diet on 10-year atherosclerotic cardiovascular disease Risk (from the DASH Trial). Am J Cardiol. 2023;187:10–17. doi: 10.1016/j.amjcard.2022.10.019
10. Shah RV, Murthy VL, Colangelo LA, Reis J, Venkatesh BA, Sharma R, Abbasi SA, Goff DC, Carr JJ, Rana JS, et al. Association of fftness in young adulthood with survival and cardiovascular risk: the Coronary Artery Risk Development in Young Adults (CARDIA) study. JAMA Intern Med. 2016;176:87–95. doi: 10.1001/jamainternmed.2015.6309
11. Hamm LL, McCullough PA, Kasiske BL, Kelepouris E, Klag MJ, Parfrey P, Pfeffer M, Raij L, Spinosa DJ, Wilson PW. Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention. Circulation. 2003;108:2154–2169. doi: 10.1161/01.CIR.0000095676.90936.80
12. Kannel WB, McGee DL. Diabetes and cardiovascular disease: the Framingham study. JAMA. 1979;241:2035–2038. doi: 10.1001/jama.241.19.2035
13. Sinha A, Ning H, Ahmad FS, Bancks MP, Carnethon MR, O’Brien MJ, Allen NB, Wilkins JT, Lloyd-Jones DM, Khan SS. Association of fasting glucose with lifetime risk of incident heart failure: the Lifetime Risk Pooling Project. Cardiovasc Diabetol. 2021;20:66. doi: 10.1186/s12933-021-01265-y
14. Sinha A, Ning H, Cameron N, Bancks M, Carnethon MR, Allen NB, Wilkins JT, Lloyd-Jones DM, Khan SS. Atherosclerotic cardiovascular disease or heart failure: ffrst cardiovascular event in adults with prediabetes and diabetes. J Card Fail. 2023;29:246–254. doi: 10.1016/j.cardfail.2022.10.426
15. Sinha A, Ning H, Carnethon MR, Allen NB, Wilkins JT, Lloyd-Jones DM, Khan SS. Race-and sex-speciffc population attributable fractions of incident heart failure: a population-based cohort study from the Lifetime Risk Pooling Project. Circ Heart Fail. 2021;14:e008113. doi: 10.1161/CIRCHEARTFAILURE.120.008113
16. Imai Y, Sakurai M, Nakagawa H, Hirata A, Murakami Y, Kiyohara Y, Ninomiya T, Ishikawa S, Saitoh S, Irie F, et al. Impact of proteinuria and low eGFR on lifetime risk of cardiovascular disease death: a pooled analysis of data from the Evidence for Cardiovascular Prevention From Observational Cohorts in Japan Study. Eur J Prev Cardiol. 2021;28:zwab061. doi: 10.1093/eurjpc/zwab061.179
17. ?stergaard HB, Read SH, Sattar N, Franzén S, Halbesma N, Dorresteijn JA, Westerink J, Visseren FL, Wild SH, Eliasson B, et al. Development and validation of a lifetime risk model for kidney failure and treatment benefft in type 2 diabetes: 10-year and lifetime risk prediction models. Clin J Am Soc Nephrol. 2022;17:1783–1791. doi: 10.2215/cjn.05020422
18. Khan SS, Krefman AE, Zhao L, Liu L, Chorniy A, Daviglus ML, Schiman C, Liu K, Shih T, Garside D, et al. Association of body mass index in midlife with morbidity burden in older adulthood and longevity. JAMA Netw Open. 2022;5:e222318. doi: 10.1001/jamanetworkopen.2022.2318
19. Khan SS, Ning H, Wilkins JT, Allen N, Carnethon M, Berry JD, Sweis RN, Lloyd-Jones DM. Association of body mass index with lifetime risk of cardiovascular disease and compression of morbidity. JAMA Cardiol. 2018;3:280– 287. doi: 10.1001/jamacardio.2018.0022
20. Allen NB, Zhao L, Liu L, Daviglus M, Liu K, Fries J, Shih Y-CT, Garside D, Vu T-H, Stamler J, et al. Favorable cardiovascular health, compression of morbidity, and healthcare costs: forty-year follow-up of the CHA study (Chicago Heart Association Detection Project in Industry). Circulation. 2017;135:1693–1701. doi: 10.1161/circulationaha.116.026252
21. Matsushita K, Jassal SK, Sang Y, Ballew SH, Grams ME, Surapaneni A, Arnlov J, Bansal N, Bozic M, Brenner H, et al. Incorporating kidney disease measures into cardiovascular risk prediction: development and validation in 9 million adults from 72 datasets. EClinicalMedicine. 2020;27:100552. doi: 10.1016/j.eclinm.2020.100552
22. Fangel MV, Nielsen PB, Kristensen JK, Larsen TB, Overvad TF, Lip GY, Jensen MB. Albuminuria and risk of cardiovascular events and mortality in a general population of patients with type 2 diabetes without cardiovascular disease: a Danish cohort study. Am J Med. 2020;133:e269–e279. doi: 10.1016/j.amjmed.2019.10.042
23. Lees JS, Welsh CE, Celis-Morales CA, Mackay D, Lewsey J, Gray SR, Lyall DM, Cleland JG, Gill JM, Jhund PS. Glomerular ffltration rate by differing measures, albuminuria and prediction of cardiovascular disease, mortality and end-stage kidney disease. Nat Med. 2019;25:1753–1760. doi: 10.1038/s41591-019-0627-8
24. Khera R, Pandey A, Ayers CR, Carnethon MR, Greenland P, Ndumele CE, Nambi V, Seliger SL, Chaves PH, Safford MM. Performance of the pooled cohort equations to estimate atherosclerotic cardiovascular disease risk by body mass index. JAMA Netw Open. 2020;3:e2023242. doi: 10.1001/jamanetworkopen.2020.23242
25. Goh LG, Dhaliwal SS, Welborn TA, Lee AH, Della PR. Anthropometric measurements of general and central obesity and the prediction of cardiovascular disease risk in women: a cross-sectional study. BMJ Open. 2014;4:e004138. doi: 10.1136/bmjopen-2013-004138
26. Mehta A, Virani SS, Ayers CR, Sun W, Hoogeveen RC, Rohatgi A, Berry JD, Joshi PH, Ballantyne CM, Khera A. Lipoprotein (a) and family history predict cardiovascular disease risk. J Am Coll Cardiol. 2020;76:781–793. doi: 10.1016/j.jacc.2020.06.040
27. Reyes-Soffer G, Ginsberg HN, Berglund L, Duell PB, Heffron SP, Kamstrup PR, Lloyd-Jones DM, Marcovina SM, Yeang C, Koschinsky ML; on behalf of the American Heart Association Council on Arteriosclerosis, Thrombosis and Vascular Biology; Council on Cardiovascular Radiology and Intervention; and Council on Peripheral Vascular Disease. Lipoprotein (a): a genetically determined, causal, and prevalent risk factor for atherosclerotic cardiovascular disease: a scientiffc statement from the American Heart Association. Arterioscler Thromb Vasc Biol. 2022;42:e48–e60. doi: 10.1161/ATV.0000000000000147 28. Patel AP, Wang M, Pirruccello JP, Ellinor PT, Ng K, Kathiresan S, Khera AV. Lp (a) (lipoprotein [a]) concentrations and incident atherosclerotic cardiovascular disease: new insights from a large national biobank. Arterioscler Thromb Vasc Biol. 2021;41:465–474. doi: 10.1161/ATVBAHA.120.315291
29. Pop-Busui R, Januzzi JL, Bruemmer D, Butalia S, Green JB, Horton WB, Knight C, Levi M, Rasouli N, Richardson CR. Heart failure: an underappreciated complication of diabetes. A consensus report of the American Diabetes Association. Diabetes Care. 2022;45:1670–1690. doi: 10.2337/dci22-0014
30. Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, Himmelfarb CD, Khera A, Lloyd-Jones D, McEvoy JW, et al. 2019 ACC/ AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;140:e596–e646. doi: 10.1161/CIR.0000000000000678
31. Khan SS, Page C, Wojdyla DM, Schwartz YY, Greenland P, Pencina MJ. Predictive utility of a validated polygenic risk score for long-term risk of coronary heart disease in young and middle-aged adults. Circulation. 2022;146:587–596. doi: 10.1161/CIRCULATIONAHA.121.058426
32. Khan SS, Post WS, Guo X, Tan J, Zhu F, Bos D, Sedaghati-Khayat B, Van Rooij J, Aday A, Allen NB, et al. Coronary artery calcium score and polygenic risk score for the prediction of coronary heart disease events. JAMA. 2023;329:1768–1777. doi: 10.1001/jama.2023.7575
声明:本文仅供医疗卫生专业人士了解最新医药资讯参考使用,不代表本平台观点。该等信息不能以任何方式取代专业的医疗指导,也不应被视为诊疗建议,如果该信息被用于资讯以外的目的,本站及作者不承担相关责任。
最新《国际糖尿病》读者专属微信交流群建好了,快快加入吧!扫描左边《国际糖尿病》小助手二维码(微信号:guojitnb),回复“国际糖尿病读者”,ta会尽快拉您入群滴!
(来源:《心肾代谢时讯》编辑部)