多因素分析方法在控制混杂因素中的应用

混杂是临床研究中重要的偏倚来源之一,在观察性或干预性糖尿病研究中均不可避免。多因素分析方法是数据分析阶段常见的校正混杂因素的统计学方法,笔者将以糖尿病研究为例,介绍几种常见的多因素分析方法及运用多因素分析方法校正混杂因素时需要注意的问题。     

动态观察臂踝动脉脉搏波传导速度变化来预测青年人血压的改变

目的观察臂踝动脉脉搏波传导速度(baPWV)对青年人血压进展的影响。方法以2010-2013年(第1次)健康体检且同期参加baPWV检测的指标作为基数,于2014-2015(第2次)健康体检指标作为随访资料,进行对照研究,应用多元线性回归分析及多元Logistic回归分析baPWV与青年人血压进展的相关性。结果①符合入选标准的研究对象共2784例,年龄(38.1±5.1)岁,其中男性2208例(79.3%)。②在多元线性回归分析中,校正其他混杂因素后,baPWV水平不仅与基线及随访血压值呈正相关,而且与它们的差值(Δ收缩压、Δ舒张压)呈线性关系。基线baPWV每增加1.0 m/s,基线收缩压和舒张压分别增加2.01和1.22 mm Hg;随访收缩压和舒张压分别增加1.79和0.86 mm Hg;Δ收缩压和Δ舒张压分别增加0.58和0.26 mm Hg。③在多因素Logistic回归分析中,校正其他混杂因素后,动脉硬化人群新发高血压的风险是非动脉硬化人群的1.29倍(95%CI 1.17～1.39);基线baPWV每增加1个标准差,新发高血压的风险增加至1.76倍(OR=1.76,95%CI 1.53～2.03)。结论动态观察baPWV变化可以反映血压变化及动脉硬化的进展。     

老年人群癌症与心血管疾病因果关联的孟德尔随机化研究

目的 采用孟德尔随机化方法(MR)分析欧洲60岁以上人群癌症(乳腺癌、肺癌、胃癌、皮肤癌、前列腺癌)与10种常见心血管病的因果联系。方法 通过公共全基因组关联分析(GWAS)数据库获得工具变量，样本均来自欧洲人群，以逆方差加权法为主要方法进行两样本MR分析，并对结果行敏感性分析。后选择糖尿病、吸烟、体质量指数(BMI)3个混杂因素对癌症进行校正，并行多样本MR研究。结果 通过两样本的MR分析共得到5种检验效能小于0.05的因果关联，分别是乳腺癌与心肌梗死、乳腺癌与脑梗死、肺癌与脑梗死、肺癌与房颤、肺癌与外周动脉疾病。在多样本MR分析中，调整混杂因素后，乳腺癌对心肌梗死，肺癌对外周动脉疾病的效应值仍然较强。结论 在欧洲老年人群中乳腺癌与心肌梗死的发生可能存在负相关风险，肺癌与外周动脉疾病发生率可能存在正相关风险。     

老年心脏瓣膜病的现状

近年来随着心脏瓣膜病研究的进展,对老年心脏瓣膜病的临床认识亦日益深入,现仅就常见的几种老年心脏瓣膜病作一简述。 老年风湿性心脏瓣膜病 风心病是从儿童期至老年期的常见病,在老年病临床中占有重要地位。     

老年急性脑梗死患者营养不良与静脉溶栓后出血转化的相关性

目的 探讨老年急性脑梗死(ACI)患者营养不良与静脉溶栓(IVT)后出血转化之间的相关性。方法 回顾性队列研究，选择接受IVT治疗的老年ACI患者370例，根据控制营养状况(CONUT)评分分为正常营养组(CONUT 0～1分)180例、轻度营养不良组(CONUT 2～4分)153例和中-重度营养不良组(CONUT 5～12分)37例。采用单因素和多因素的稀有事件Logistic回归评估营养不良与IVT治疗后出血转化的相关性。结果 3组症状性颅内出血(SICH)发生率差异有统计学意义(P<0.001),且中-重度营养不良组SICH发生率显著高于正常营养组和轻度营养不良组(P<0.05)。多因素稀有事件Logistic回归校正混杂因素后，中-重度营养不良与SICH显著相关(OR=4.21,95%CI:1.35～13.07,P=0.013)。结论 中-重度营养不良(CONUT 5～12分)是老年ACI患者IVT治疗后SICH的一个独立危险因素。     

左室射血分数与老年慢性心力衰竭患者认知功能的关系

目的 探索左室射血分数与老年慢性心力衰竭患者认知功能的关系。方法 人选左室射血分数小于45%且年龄大于或等于60岁的住院慢性心衰患者共222例。使用简易智能量表(Mini-Mental State Examination,MMSE) 进行认知功能评估。收集相关临床资料。采用logistic回归分析校正混杂因素的影响。结果 以 MMSE评分<24分作为认知功能障碍的界值,46.4%(103/222)的患者存在认知功能障碍。多因素分析结果显示,在以年龄、受教育情况、长期饮酒史、伴发糖尿病史校正后,左心室射血分数减低可以增加心衰患者认知功能障碍的风险(OR=0.92,95%CI：0.87~0.97)。结论 慢性心衰患者认知障碍发生率较高,左室射血分数减低是慢性心衰患者发生认知功能障碍的独立危险因素。     

老年人脑微出血与收缩压晨峰的相关性

目的探讨收缩压晨峰对老年人脑微出血(CMB)的影响。方法 2008年4月~2009年10月于山东省医学科学院心脑血管病防治研究中心选择年龄≥60岁的健康体检的老年人408例。采用24h动态血压监测血压晨峰,根据收缩压晨峰,将受试者分为晨峰组169例[收缩压晨峰≥35mm Hg(1mm Hg=0.133kPa)]和非晨峰组239例(收缩压晨峰<35mm Hg)。分别于2008~2009年(基线)、2010~2012年(随访)和2013~2015年(随访)共进行3次头颅MRI检查评估CMB。患者随访39~72(62.04±6.80)个月,将随访与基线比较,有新发CMB定义为CMB进展。用logistic回归分析影响因素,用Kaplan-Meier生存函数曲线分析,用多元Cox生存回归分析。结果晨峰组诊室收缩压、24h平均收缩压、昼间平均收缩压、昼间平均舒张压、收缩压晨峰、舒张压晨峰和LDL-C水平及CMB患病率显著高于非晨峰组(P<0.05,P<0.01)。logistic回归分析在校正相关混杂因素后,晨峰组患CMB的危险显著高于非晨峰组(OR=2.561,95%CI:1.142~5.743,P=0.019)。晨峰组累积CMB进展率显著高于非晨峰组(18.5%vs 7.6%,χ^2=7.954,Plog-rank=0.005)。在校正包括基线有无CMB在内的相关混杂因素后,晨峰组发生CMB进展的风险显著增高,是非晨峰组的2.353倍(95%CI:1.317~3.197,P=0.002)。结论收缩压晨峰是CMB患病及进展的独立危险因素,过高的收缩压晨峰促进老年人CMB的发展。     

臂踝动脉脉搏波传导速度与认知功能的关联性

目的探讨臂踝动脉脉搏波传导速度(baPWV)与认知功能的关联。方法选取开滦研究中在2012-2013年度健康体检中进行了baPWV及简易智力状态检查量表(MMSE)检测的2928例观察对象纳入分析。采用多因素线性回归和多因素Logistic回归方法分析baPWV与MMSE评分的关联和baPWV增加对MMSE评分的影响。结果观察对象2928例。其中,1032例(35.2%)baPWV14m/s,1896例(64.8%)baPWV≥14m/s。baPWV与MMSE评分呈负相关。在多因素线性回归分析中,校正其他混杂因素后,年龄≥60岁人群baPWV与MMSE评分呈负相关(β=-0.058,P=0.041)。在多因素Logistic回归分析中,校正其他混杂因素后,结果发现:年龄≥60岁人群中,baPWV每增加1 m/s,发生MMSE评分降低的OR值(95%CI)为1.02(1.01~1.14)。结论年龄≥60岁人群中,baPWV与MMSE评分存在负相关,baPWV升高可能增加认知功能下降的发生风险。     

贫血对老年2型糖尿病患者肾功能的影响

目的本文旨在了解老年2型糖尿病(T2DM)患者贫血的患病情况,以及其对患者肾功能损害的长期影响。方法选取于2009年1月至2013年12月参加本院糖尿病中心分阶段糖尿病达标管理(SDTM)项目随访管理研究的患者,共收集505例老年T2DM患者,所有观察对象均进行定期随访,每年复查血常规及血清肌酐水平。采用MDRD公式计算肾小球滤过率估计值(eGFR)。结果本组患者中位随访时间为17(11~31)月。基线时T2DM患者的贫血患病率为23.6%,合并贫血者女性比例较多,年龄偏大,同时糖尿病(DM)病程较长。伴贫血者尿微量蛋白水平、血清肌酐值均显著高于无贫血者,同时eGFR显著低于无贫血组。所有T2DM患者eGFR都逐年下降,2组eGFR逐年下降率相近。随访期间,本组患者共发生肾功能损害进展事件11例,将糖尿病不伴有贫血发生肾功能损害进展的相对风险设定为1(参照),则糖尿病伴有贫血患者的相对风险(RR)为6.435,95%CI为1.825~22.691,校正基线状态eGFR、年龄、性别、糖化血红蛋白(HbA1c)等因素后,伴有贫血患者的RR仍显著升高至5.04倍。结论老年T2DM患者具有较高的贫血患病率,应当引起临床医师的重视。老年T2DM贫血者多伴有早期肾脏损害,经多因素校正后,贫血是老年T2DM患者肾脏损害进展的危险因素。     

青年冠心病研究进展

冠心病已经成为危害全人类健康的常见病及多发病,并且发病年龄越来越年轻。青年冠心病与老年冠心病不同,有自己的特点。国外关于它的研究相对较多,而国内相关报道较少。现就青年冠心病的危险因素,易感基因以及干细胞移植治疗方面的进展作一综述。     

Recent Findings from Mendelian Randomization Studies of Cardiovascular Disease

Judgments as to causality of associations between risk factors and disease involve a weighing of the available evidence from animal and human studies. The human data include observational studies, results of which may be confounded, and randomized trials, which may be unavailable or infeasible, thus limiting the ability to make causal inference. Mendelian randomization offers an additional approach to help assess causality. The concept relies on the random assignment of alleles at meiosis as the basis for a natural randomized experiment linking genetic variants to intermediate risk factors and disease. Recent Mendelian randomization studies have provided evidence for a causal role of serum lipoprotein(a) in coronary heart disease, and against a causal role for plasma C-reactive protein. Although a valid Mendelian randomization experiment is subject to a number of assumptions and limitations, it is a useful adjunct to assess causality, especially when randomized trials are unavailable.     

Mendelian Randomization: How Genetics Is Pushing the Boundaries of Epidemiology to Identify New Causes of Heart Disease

The past 10 years have seen a remarkable revolution in the genetics of cardiovascular (CV) disease. Although much work remains to bring these discoveries to the bedside, genetics has opened up remarkable possibilities in understanding the causes of CV disease through a relatively novel study design known as “Mendelian randomization.” Akin to a randomized trial, Mendelian randomization is a genetic study design that takes advantage of the “randomization” of genetic information at birth to evaluate a potential causal relationship between a genetically determined biomarker and an outcome. By providing evidence for causal relationships, Mendelian randomization can improve our understanding of fundamental mechanisms in human disease, potentially accelerate the identification of bona fide drug targets, and ultimately improve the care of patients with CV disease. This review describes the concept and design of Mendelian randomization genetic studies, discusses their strengths and weaknesses, and presents recent examples of Mendelian randomization studies in the CV literature that have helped clarify the causal role of selected biomarkers in CV medicine.     

Causal inference in obesity research

Obesity is a risk factor for a plethora of severe morbidities and premature death. Most supporting evidence comes from observational studies that are prone to chance, bias and confounding. Even data on the protective effects of weight loss from randomized controlled trials will be susceptible to confounding and bias if treatment assignment cannot be masked, which is usually the case with lifestyle and surgical interventions. Thus, whilst obesity is widely considered the major modifiable risk factor for many chronic diseases, its causes and consequences are often difficult to determine. Addressing this is important, as the prevention and treatment of any disease requires that interventions focus on causal risk factors. Disease prediction, although not dependent on knowing the causes, is nevertheless enhanced by such knowledge. Here, we provide an overview of some of the barriers to causal inference in obesity research and discuss analytical approaches, such as Mendelian randomization, that can help to overcome these obstacles. In a systematic review of the literature in this field, we found: (i) probable causal relationships between adiposity and bone health/disease, cancers (colorectal, lung and kidney cancers), cardiometabolic traits (blood pressure, fasting insulin, inflammatory markers and lipids), uric acid concentrations, coronary heart disease and venous thrombosis (in the presence of pulmonary embolism), (ii) possible causal relationships between adiposity and gray matter volume, depression and common mental disorders, oesophageal cancer, macroalbuminuria, end‐stage renal disease, diabetic kidney disease, nuclear cataract and gall stone disease, and (iii) no evidence for causal relationships between adiposity and Alzheimer's disease, pancreatic cancer, venous thrombosis (in the absence of pulmonary embolism), liver function and periodontitis.     

Clinical Implications of Lipid Genetics for Cardiovascular Disease

Cardiovascular disease is the leading cause of morbidity and mortality in the developed world. Epidemiologic data support a strong relationship of atherosclerotic cardiovascular disease (ASCVD) with both elevated low-density lipoprotein cholesterol (LDL-C), and reduced high-density lipoprotein cholesterol (HDL-C). The study of the human genetics of plasma lipid traits, both rare Mendelian disorders as well as common variants, has illuminated multiple genes and pathways involved in the regulation of LDL-C and HDL-C levels. Mendelian disorders of extremes of LDL-C and Mendelian randomization studies of common gene variants associated with LDL-C strongly support a causal relationship between LDL-C and ASCVD, independent of mechanism. In contrast, Mendelian disorders of extremes of HDL-C and Mendelian randomization studies of common genetic variants for HDL-C are inconsistent in their support of a causal relationship between HDL-C and ASCVD. In contrast to LDL-C, a causal relationship between HDL-C and ASCVD may be dependent on the specific mechanism leading to variation in HDL-C levels.     

Where is bone science taking us?

Bone science has over the last decades unraveled many important pathways in bone and mineral metabolism and the interplay between genetic factors and the environment. Some of these discoveries have led to the development of pharmacological treatments of osteoporosis and rare bone diseases. Other scientific avenues have uncovered a role for the gut microbiome in regulating bone mass, which have led to investigations on the possible therapeutic role of probiotics in the prevention of osteoporosis. Huge advances have been made in identifying the genes that cause rare bone diseases, which in some cases have led to therapeutic interventions. Advances have also been made in understanding the genetic basis of the more common polygenic bone diseases, including osteoporosis and Paget's disease of bone (PDB). Polygenic profiles are used for establishing genetic risk scores aiming at early diagnosis and intervention, but also in Mendelian randomization (MR) studies to investigate both desired and undesired effects of targets for drug design.     

Milk consumption and the risk of type 2 diabetes: A systematic review of Mendelian randomization studies

AimsPreviously, no relationship between milk consumption and the risk of type 2 diabetes has been found in prospective cohorts. However, Mendelian randomization allows researchers to almost bypass much residual confounding, providing a more precise effect estimate.This systematic review aims to investigate the risk of type 2 diabetes and levels of HbA1c by assessing all Mendelian Randomization studies investigating this subject matter.Data synthesisPubMed and EMBASE were searched from October 2021 through February 2023. Inclusion and exclusion criteria were formulated to filter out irrelevant studies. Studies were qualitatively assessed with STROBE-MR together with a list of five MR criteria. Six studies were identified, containing several thousand participants. All studies used the SNP rs4988235 as the main exposure and type 2 diabetes and/or HbA1c as the main outcome. Five studies were graded as “good” with STROBE-MR, with one graded as “fair”. For the six MR criteria, five studies were graded “good” in four criteria, while two studies were graded “good” in two criteria. Overall, genetically predicted milk consumption did not seem to be associated with an increased risk of type 2 diabetes.ConclusionsThis systematic review found that genetically predicted milk consumption did not seem to increase the risk of type 2 diabetes. Future Mendelian randomization studies concerning this topic should consider conducting two-sample Mendelian Randomization studies, in order to derive a more valid effect estimate.     

Higher Cholesterol Level Predicts Cardiovascular Event and Inversely Associates With Mortality in Hemodialysis Patients: 10‐Year Outcomes of the Q‐Cohort Study

The prevalence of atherosclerotic diseases is higher in hemodialysis patients. The aim of the current study was to investigate associations between cholesterol level and the incidences of cardiovascular disease (CVD) and mortality in hemodialysis patients. A total of 3517 participants undergoing maintenance hemodialysis were followed up for 10 years. Total cholesterol (TC) level was divided into quartile in baseline data. The multivariate analyses were calculated by a Cox proportional hazards model. The incidences of ischemic heart disease (IHD), peripheral artery disease (PAD), and CVD were significantly positively associated with higher cholesterol levels after adjustment for confounding factors (P < 0.01, P = 0.04, and P < 0.01, respectively). Furthermore, the incidences of cancer‐associated mortality and all‐cause mortality were significantly positively associated with lower cholesterol levels after adjustment for confounding factors (both P < 0.01). The lowest TC level at all‐cause mortality risk was 179 mg/dL. From these results, higher TC predicts IHD, PAD, and CVD events, and lower TC predicts cancer‐associated mortality and all‐cause mortality in patients undergoing maintenance hemodialysis.     

The homocysteine controversy

Mild to moderate hyperhomocysteinemia has been identified as a strong predictor of cardiovascular disease, independent from classical atherothrombotic risk factors. In the last decade, a number of large intervention trials using B vitamins have been performed and have shown no benefit of homocysteine-lowering therapy in high-risk patients. In addition, Mendelian randomization studies failed to convincingly demonstrate that a genetic polymorphism commonly associated with higher homocysteine levels (methylenetetrahydrofolate reductase 677 C>T) is a risk factor for cardiovascular disease. Together, these findings have cast doubt on the role of homocysteine in cardiovascular disease pathogenesis, and the homocysteine hypothesis has turned into a homocysteine controversy. In this review, we attempt to find solutions to this controversy. First, we explain that the Mendelian randomization analyses have limitations that preclude final conclusions. Second, several characteristics of intervention trials limit interpretation and generalizability of their results. Finally, the possibility that homocysteine lowering is in itself beneficial but is offset by adverse side effects of B vitamins on atherosclerosis deserves serious attention. As we explain, such side effects may relate to direct adverse effects of the B-vitamin regimen (in particular, the use of high-dose folic acid) or to proinflammatory and proproliferative effects of B vitamins on advanced atherosclerotic lesions.     

The Genetic Link Between Diabetes and Atherosclerosis

Epidemiological studies have indicated that the risk of atherothrombotic coronary artery disease (CAD) is higher in patients with diabetes, but these results have not been consistently observed across clinical trials. To address this apparent discrepancy, we can apply the results of genome-wide association studies (GWAS) to provide a better understanding of the shared genetic architecture of diabetes and atherothrombotic CAD. For instance, a large GWAS has identified 16 novel loci that are associated with both diabetes and atherothrombotic CAD. These genetic variants may also be used to assess potential causal relationships reported in observational studies and clinical trials through Mendelian randomization analyses. For example, several Mendelian randomization analyses have shown that diabetes is associated with CAD independent of other risk factors (odds ratio [OR]: 1.63, 95% confidence interval [CI]: 1.23–2.07; P = 0.002). Furthermore, Mendelian randomization analyses can provide more insight into the perceived risk of diabetes among patients without diabetes receiving statin therapy. Here, genetically lower activity of HMG-CoA reductase (HMGCR) was associated with a modest increase in diabetes (OR per allele: 1.02, 95% CI: 1.00–1.05). These results highlight the biological mechanisms that link diabetes with the use of statins. In addition, this work illustrates the great potential value of genetic studies to clarify the mechanistic relationships among atherosclerotic vascular disease, dysglycemia, and diabetes. More research is needed to delineate and subsequently better understand the genetic links between diabetes and atherosclerosis.     

The genetics of the autoimmune thyroid diseases.

AIM: To understand the modern approach to finding the genes responsible for complex diseases when compared to simple Mendelian inheritance. To define the autoimmune thyroid diseases as we now understand them and introduce the concept of disease heterogeneity. To review the evidence for a genetic contribution to autoimmune thyroid disease as opposed to environmental factors and to be aware of new information generated from recent studies concerning the role of HLA, immune modulators, and new loci of interest providing susceptibility to the autoimmune thyroid diseases.