A Clinical Study of Structural Properties of Osteosarcopenic Obesity Syndrome Using Multivariate Statistical Methods

doi:10.12114/j.issn.1007-9572.2022.0152

Chinese General Practice ›› 2022, Vol. 25 ›› Issue (22): 2733-2739.DOI: 10.12114/j.issn.1007-9572.2022.0152

Special Issue: 骨健康最新文章合辑；肥胖最新文章合辑

• Original Research·Clinical Quality Improvement • Previous Articles Next Articles

A Clinical Study of Structural Properties of Osteosarcopenic Obesity Syndrome Using Multivariate Statistical Methods

¹Physical Examination Center, the 2nd Affiliated Hospital of Harbin Medical University, Harbin 150086, China
²School of Health Management, Harbin Medical University, Harbin 150081, China

Received:2021-12-17 Revised:2022-05-30 Published:2022-08-05 Online:2022-06-23
Contact: Qunhong WU
About author:
NIE Y Z, YAN Z Q, YAN W, et al. A clinical study of structural properties of osteosarcopenic obesity syndrome using multivariate statistical methods[J]. Chinese General Practice, 2022, 25 (22) : 2733-2739, 2745.

基于多元统计学方法的骨量肌量减少性肥胖综合征的结构特征研究

¹150086　黑龙江省哈尔滨市，哈尔滨医科大学附属第二医院体检中心
²150081　黑龙江省哈尔滨市，哈尔滨医科大学卫生管理学院

通讯作者: 吴群红
作者简介:
聂义珍，闫朝岐，燕巍，等. 基于多元统计学方法的骨量肌量减少性肥胖综合征的结构特征研究[J]. 中国全科医学，2022，25（22）：2733-2739，2745.[www.chinagp.net] 作者贡献：聂义珍提出研究思路，设计研究方案，负责研究实施和论文撰写；燕巍、赵兴鹃负责数据收集和整理；闫朝岐、付红梅负责论文的修订；聂义珍、尹慧负责统计学分析；吴群红负责文章的质量控制与审校，并对文章整体负责，监督管理。
基金资助:
黑龙江省卫生计生委科研课题(2018164)

Abstract

Abstract:

Background

Osteosarcopenic obesity syndrome (OSO) is a disease that seriously endangers the health of older people. The rational classification of the disease can guide the clinical diagnosis and treatment. Therefore, classifying OSO based on inter-correlations of its diagnostic variables and exploring its structural properties may offer insights into clinical prevention and treatment of OSO.

Objective

To explore the structural properties of OSO, providing a theoretical basis for individualized diagnosis and treatment of the disease.

Methods

A cross-sectional study was conducted with a random sample of OSO patients (≥60 years old) who underwent physical examination in Physical Examination Center, the 2nd Affiliated Hospital of Harbin Medical University from January 2018 to December 2020. The data collected include 9 diagnostic variables for OSO〔skeletal muscle index, grip strength, body fat percentage, BMD of the lumbar spine (L1-L4), hip and femoral neck, BMI, waist circumference, walking pace〕, sociodemographic characteristics, lifestyle and prevalence of common chronic diseases. KMO test and Bartlett's test of sphericity were used to evaluate the suitability of diagnostic variables for factor analysis. The components with an eigenvalue equal to or greater than 1.000 were extracted by principal component analysis, and the varimax orthogonal rotation matrix was obtained by the varimax orthogonal rotation method. The common factors were named according to the orthogonal rotation matrix of factors. On the basis of factor analysis, thesum of squares and systematic cluster analysis were used to develop a dendrogram for classifying patients. The structural properties of OSO were analyzed by comparing the values of diagnostic variables and clinical features among patients of different categories.

Results

A total of 107 cases were included. The KMO value (0.688) and the result of Bartlett's test of sphericity (χ²=492.374, P<0.001) indicated that the data of diagnostic variables were suitable for factor analysis. Three common factors (osteoporosis factor, muscle + body fat factor and obesity factor) with an eigenvalue greater than 1.000 were extracted, explaining 81.408% variance of the total. The load value of each diagnostic variable on its common factor ranged from 0.770 to 0.918. The patients were divided into 3 categoriesby cluster analysis using the common factors. The skeletal muscle index, grip strength, body fat percentage, BMD of L1-L4, hip and femoral neck, BMI and waist circumference varied significantly across patients of different categories (P<0.05). The values of BMD of L1-L4, hip and femoral neck of OSO patients in the first category were significantly lower than those of the other two categories (P<0.05). The BMI and waist circumference values of OSO patients in the second category were lower than those of the other two categories (P<0.05). OSO patients in the third category had higher values of skeletal muscle index, grip strength and BMD of L1-L4, hip and femoral neck, but lower body fat percentage than those of the other two categories (P<0.05). There were statistically significant differences in sex ratio, distribution of education level and total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), uric acidand creatinine in the serum among different categories of patients (P<0.05). OSO patients in the first category had higher prevalence of below the undergraduate education level than those in the third category (P<0.017). OSO patients in the second category had higher level of TC than those in the third category (P<0.05). In comparison with those in other two categories, OSO patients in the third category had higher personal monthly income equal to or greater than 5 000 yuan, and lower female ratio (P<0.017). Moreover, OSO patients in the third category also demonstrated higher levels of uric acid and creatinine in the serum (P<0.05) .

Conclusion

OSO diagnostic variables can be generalized and interpreted in terms of osteoporosis, muscle and body fat, and obesity. And OSO patients have different structural properties. The application of multivariate statistical methods to study the structural properties of OSO patients will contribute to the individualized management of such patients.

Key words: Osteosarcopenic obesity, Factor analysis, Cluster analysis, Multivariate statistical analysis

摘要：

背景

骨量肌量减少性肥胖综合征（OSO）是一种严重损害老年人健康的疾病，对疾病进行临床分型可为疾病的临床诊治提供指导。基于OSO诊断变量间的相关性对OSO进行分型，并探寻OSO的结构特征，可为OSO的防治提供新的思路。

目的

探索OSO的结构特征，为实现OSO的个体化诊治提供理论依据。

方法

本研究为横断面研究。于2018年1月至2020年10月，采用随机抽样法，选取在哈尔滨医科大学附属第二医院体检中心接受健康体检、年龄≥60岁的老年OSO患者作为研究对象，采集其OSO诊断变量〔四肢骨骼肌指数，握力，体脂百分比（BF%），腰椎1~4（L_1~4）、髋部、股骨颈骨密度（BMD），体质指数（BMI），腰围，步速〕、社会人口学特征、生活方式、常见慢性病患病情况等方面的资料。在利用因子分析法对OSO诊断变量数据进行分析前，采用KMO检验、Bartlett's球形检验评价OSO诊断变量数据是否适合进行因子分析。通过主成分分析法，提取特征值≥1.000的成分，并运用最大方差正交旋转法得出方差最大正交旋转矩阵。根据因子正交旋转矩阵，对公因子进行命名。基于公因子得分，利用离差平方和系统聚类法生成树状结构并对患者进行分类，通过比较不同类别患者间诊断变量水平和临床特征的差异，分析OSO的结构特征。

结果

共纳入107例老年OSO患者。KMO值为0.688，Bartlett's球形检验χ²=492.374，P<0.001，表明OSO诊断变量数据适合进行因子分析；按特征根>1.000的标准可提取3个公因子（骨质疏松因子、肌肉+体脂因子、肥胖因子），3个公因子的累积方差贡献率为81.408%，各诊断变量在所属公因子上的载荷值为0.770~0.918。聚类分析结果显示，共将OSO患者分为3类。不同类别人群四肢骨骼肌指数、握力、BF%、BMD_L1~4、BMD_髋部、BMD_股骨颈、BMI、腰围比较，差异均有统计学意义（P<0.05）。其中第1类人群的BMD_L1~4、BMD_髋部、BMD_股骨颈均低于其他两类人群（P<0.05）；第2类人群的BMI和腰围均低于其他两类人群（P<0.05）；第3类人群的四肢骨骼肌指数、握力和BMD均高于其他两类人群，BF%低于其他两类人群（P<0.05）。不同类别人群性别、受教育程度、个人月收入分布、总胆固醇（TC）、高密度脂蛋白胆固醇（HDL-C）、尿酸（UA）、肌酐（Cr）水平比较，差异有统计学意义（P<0.05）。第1类人群中本科以下学历者占比高于第3类人群（P<0.017）；第2类人群TC水平高于第3类人群（P<0.05）；第3类人群个人月收入≥5 000元者占比，以及UA、Cr水平均高于其他两类人群，女性占比低于其他两类人群（P<0.05或P<0.017）。

结论

可从骨质疏松、肌肉与体脂、肥胖3个方面对OSO诊断变量进行概括和解释；OSO患者具有不同的结构特征。应用多元统计学方法研究OSO患者的结构特征，有助于实现对不同类型OSO患者的个体化管理。

关键词: 骨量肌量减少性肥胖综合征, 因子分析, 聚类分析, 多元统计学方法

Yizhen NIE,Zhaoqi YAN,Wei YAN, et al. A Clinical Study of Structural Properties of Osteosarcopenic Obesity Syndrome Using Multivariate Statistical Methods[J]. Chinese General Practice, 2022, 25(22): 2733-2739. DOI: 10.12114/j.issn.1007-9572.2022.0152.
聂义珍,闫朝岐,燕巍等. 基于多元统计学方法的骨量肌量减少性肥胖综合征的结构特征研究[J]. 中国全科医学, 2022, 25(22): 2733-2739. DOI: 10.12114/j.issn.1007-9572.2022.0152.

Figures/Tables 5

References 29

[1]	ORMSBEE M J，PRADO C M，ILICH J Z，et al. Osteosarcopenic obesity：the role of bone，muscle，and fat on health[J]. J Cachexia Sarcopenia Muscle，2014，5（3）：183-192. DOI：10.1007/s13539-014-0146-x.
[2]	ILICH J Z，KELLY O J，INGLIS J E. Osteosarcopenic obesity syndrome：what is it and how can it be identified and diagnosed[J]. Curr Gerontol Geriatr Res，2016，2016：7325973. DOI：10.1155/2016/7325973.
[3]	SCOTT D，CHANDRASEKARA D，LASLETT L L，et al. Associations of sarcopenicobesity and dynapenic obesity with bone mineral density and incident fractures over 5-10 years incommunity-dwelling older adults[J]. Calcif Tissue Int，2016，99（1）：30-42. DOI：10.1007/s00223-016-0123-9.
[4]	CRUZ-JENTOFT A J，BAEYENS J P，BAUER J M，et al. Sarcopenia：European consensus on definition and diagnosis. Report of the European Working Group on Sarcopenia in Older People[J]. Age Ageing，2010，39（4）：412-423. DOI：10.1093/ageing/afq034.
[5]	CHEN L K，WOO J，ASSANTACHAI P，et al. Asian Working Group for Sarcopenia：2019 consensus update on sarcopenia diagnosisand treatment[J]. J Am Med Dir Assoc，2020，21（3）：300-307，e2. DOI：10.1016/j.jamda.2019.12.012.
[6]	SCOTT D，SEIBEL M，CUMMING R，et al. Sarcopenic obesity and its temporal associations with changes in bone mineral density，incident falls and fractures in older men：the concord health and ageing in men project[J]. J Bone Miner Res，2016，32（3）：575-583. DOI：10.1002/jbmr.3016.
[7]	DE LAET C，KANIS J A，ODEN A，et al. Body mass index as a predictor of fracture risk：a meta-analysis[J]. Osteoporos Int，2005，16（11）：1330-1338. DOI：10.1007/s00198-005-1863-y.
[8]	COMPSTON J E，WATTS N B，CHAPURLAT R，et al. Obesity is not protective against fracture in postmenopausal women：GLOW[J]. Am J Med，2011，124（11）：1043-1050. DOI：10.1016/j.amjmed.2011.06.013.
[9]	MA Y，ZHANG W，HAN P，et al. Osteosarcopenic obesity associated with poor physical performance in the elderly Chinese community[J]. Clin Interv Aging，2020，15：1343-1352. DOI：10.2147/CIA.S257739.
[10]	BAE Y J. Fruit intake and osteosarcopenic obesity in Korean postmenopausal women aged 50-64 years[J]. Maturitas，2020，134：41-46. DOI：10.1016/j.maturitas.2020.02.003.
[11]	SALECH F，MARQUEZ C，LERA L，et al. Osteosarcopenia predicts falls，fractures，and mortality in Chilean community-dwelling older adults[J]. J Am Med Dir Assoc，2021，22（4）：853-858. DOI：10.1016/j.jamda.2020.07.032.
[12]	The WHO Study Group. Assessment of fracture risk and its application to screening for postmenopausal osteoporosis[EB/OL]. [2021-11-15].
[13]	CHOI B，STEISS D，GARCIA-RIVAS J，et al. Comparison of body mass index with waist circumference and skinfold-based percent body fat in firefighters：adiposity classification and associations with cardiovascular disease risk factors[J]. Int Arch Occup Environ Health，2016，89（3）：435-448. DOI：10.1007/s00420-015-1082-6.
[14]	孙振球. 医学统计学[M]. 3版. 北京：人民卫生出版社，2010.
[15]	VOGT W，NAGEL D. Cluster analysis in diagnosis[J]. Clin Chem，1992，38（2）：182-198. DOI：10.1016/0009-9120(92)80046-J.
[16]	OLIVEROS E，SOMERS V K，SOCHOR O，et al. The concept of normal weight obesity[J]. Prog Cardiovasc Dis，2014，56（4）：426-433. DOI：10.1016/j.pcad.2013.10.003.
[17]	HUNG S P，CHEN C Y，GUO F R，et al. Combine body mass index and body fat percentage measures to improve the accuracy of obesity screening in young adults[J]. Obes Res Clin Pract，2017，11（1）：11-18. DOI：10.1016/j.orcp.2016.02.005.
[18]	BEN-HUR A，GUYON I. Detecting stable clusters using principal component analysis[J]. Methods Mol Biol，2003，224：159-182. DOI：10.1385/1-59259-364-X:159.
[19]	BURGEL P R，ROCHE N，PAILLASSEUR J L，et al. Clinical COPD phenotypes identified by cluster analysis：validation with mortality[J]. Eur Respir J，2012，40（2）：495-496. DOI：10.1183/09031936.00228511.
[20]	WOOLSTON A，TU Y K，BAXTER P D，et al. A comparison of different approaches to unravel the latent structure within metabolic syndrome[J]. PLoS One，2012，7（4）：e34410. DOI：10.1371/journal.pone.0034410.
[21]	ABBASI M，NEISHABOURY M，KOOHPAYEHZADEH J，et al. National prevalence of self-reported coronary heart disease and chronic stable-angina pectoris：factor analysis of the underlying cardiometabolic risk factors in the SuRFNCD-2011[J]. Glob Heart，2018，13（2）：73-82，e1. DOI：10.1016/j.gheart.2018.01.001.
[22]	KOLIAKI C，LIATIS S，DALAMAGA M，et al. Sarcopenicobesity：epidemiologicevidence，pathophysiology，and therapeutic perspectives[J]. Curr Obes Rep，2019，8（4）：458-471. DOI：10.1007/s13679-019-00359-9.
[23]	KELLY O J，GILMAN J C，BOSCHIERO D，et al. Osteosarcopenic obesity：current knowledge，revised identification criteria and treatment principle[J]. Nutrients，2019，30，11（4）：747. DOI：10.3390/nu11040747.
[24]	中华医学会骨质疏松和骨矿盐疾病分会. 原发性骨质疏松症诊疗指南（2017）[J]. 中华骨质疏松和骨矿盐疾病杂志，2017，10（5）：413-444. DOI：10.3969/j.issn.1674-2591.2017.05.002.
[25]	中国健康促进基金会骨质疏松防治中国白皮书编委会. 骨质疏松症中国白皮书[J]. 中华健康管理学杂志，2009，3（3）：148-154. DOI：10.3760/cma.j.issn.1674-0815.2009.03.006.
[26]	袁慧琳，宋志雪，陈长香，等. 骨质疏松老年人营养状况及影响因素[J]. 中国老年学杂志，2018，38（6）：1504-1506. DOI：10.3969/j.issn.1005-9202.2018.06.092.
[27]	王晓平，丁洪流.社会经济地位是骨质疏松的决定因素之一：我们知道多少[J]. 中国骨质疏松杂志，2008，14（6）：448-453. DOI：10.3969/j.issn.1006-7108.2008.06.020.
[28]	NISHINO T，ENDO S，MIYANO H，et al. Reference serum creatinine levels according to sex，age，and height in children with down syndrome[J]. Eur J Pediatr，2021，180（9）：2977-2983. DOI：10.1007/s00431-021-04078-z.
[29]	KURAHASHI H，WATANABE M，SUGIMOTO M，et al. Testosterone replacement elevates the serum uric acid levels in patients with female to male gender identity disorder[J]. Endocr J，2013，60（12）：1321-1327. DOI：10.1507/endocrj.ej13-0203.

公因子	未经旋转的提取因子的载荷平方和			旋转后提取因子的载荷平方和
公因子	全部特征值	方差贡献率（%）	累积方差贡献率（%）	全部特征值	方差贡献率（%）	累积方差贡献率（%）
1	3.553	44.407	44.407	2.435	30.436	30.436
2	1.631	20.386	64.793	2.226	27.828	58.264
3	1.329	16.615	81.408	1.852	23.144	81.408

公因子	未经旋转的提取因子的载荷平方和			旋转后提取因子的载荷平方和
公因子	全部特征值	方差贡献率（%）	累积方差贡献率（%）	全部特征值	方差贡献率（%）	累积方差贡献率（%）
1	3.553	44.407	44.407	2.435	30.436	30.436
2	1.631	20.386	64.793	2.226	27.828	58.264
3	1.329	16.615	81.408	1.852	23.144	81.408

诊断变量	未经旋转的因子载荷			旋转后的因子载荷
诊断变量	公因子1	公因子2	公因子3	公因子1	公因子2	公因子3
四肢骨骼肌指数	0.844	0.323	-0.284	0.286	0.771	-0.471
握力	0.678	0.551	-0.123	0.183	0.845	-0.172
BF%	0.439	0.824	0.081	0.039	0.912	0.212
BMD_L1~4	0.709	-0.250	0.300	0.770	0.155	-0.197
BMD_髋部	0.785	-0.323	0.411	0.918	0.124	-0.177
BMD_股骨颈	0.716	-0.269	0.521	0.917	0.117	-0.036
BMI	-0.485	0.486	0.584	-0.162	0.059	0.884
腰围	-0.568	0.263	0.596	-0.127	-0.172	0.838

诊断变量	未经旋转的因子载荷			旋转后的因子载荷
诊断变量	公因子1	公因子2	公因子3	公因子1	公因子2	公因子3
四肢骨骼肌指数	0.844	0.323	-0.284	0.286	0.771	-0.471
握力	0.678	0.551	-0.123	0.183	0.845	-0.172
BF%	0.439	0.824	0.081	0.039	0.912	0.212
BMD_L1~4	0.709	-0.250	0.300	0.770	0.155	-0.197
BMD_髋部	0.785	-0.323	0.411	0.918	0.124	-0.177
BMD_股骨颈	0.716	-0.269	0.521	0.917	0.117	-0.036
BMI	-0.485	0.486	0.584	-0.162	0.059	0.884
腰围	-0.568	0.263	0.596	-0.127	-0.172	0.838

类别	例数	四肢骨骼肌指数〔M（QR），kg/m²〕	握力〔M（QR），kg〕	BF%〔M（QR），%〕	BMD_L1~4〔M（QR），g/cm²〕	BMD髋部（±s，g/cm²）	BMD股骨颈（±s，g/cm²）	BMI〔M（QR），kg/m²〕	腰围（±s，cm）
第1类	23	5.19（0.29）	15.50（4.10）	38.20（4.70）	0.66（0.12）	0.63±0.07	0.51±0.06	23.51（2.96）	86.35±6.10
第2类	59	5.13（0.30）	15.90（3.10）	36.90（3.00）	0.79（0.12）^a	0.75±0.08^a	0.63±0.08^a	21.93（1.88）^a	79.29±7.32^a
第3类	25	6.60（0.71）^ab	25.20（5.65）^ab	28.40（3.15）^ab	0.87（0.15）^ab	0.81±0.09^ab	0.67±0.07^ab	23.72（2.72）^b	88.84±6.52^b
H（F）值		57.308	54.580	54.293	39.207	30.110^c	31.855^c	14.333	20.208^c
P值		<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	0.001	<0.001

A Clinical Study of Structural Properties of Osteosarcopenic Obesity Syndrome Using Multivariate Statistical Methods

基于多元统计学方法的骨量肌量减少性肥胖综合征的结构特征研究

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 5

References 29

Related Articles 15

Recommended Articles

Metrics

Comments

类别	例数	年龄〔M（QR），岁〕	女性〔n（%）〕	受教育程度〔n（%）〕		个人月收入〔n（%）〕		吸烟〔n（%）〕	饮酒〔n（%）〕	规律运动〔n（%）〕	高血压〔n（%）〕
类别	例数	年龄〔M（QR），岁〕	女性〔n（%）〕	本科以下	本科及以上	<5 000元	≥5 000元	吸烟〔n（%）〕	饮酒〔n（%）〕	规律运动〔n（%）〕	高血压〔n（%）〕
第1类	23	74.0（15.0）	22（95.7）	19（82.6）	4（17.4）	19（82.6）	4（17.4）	3（13.0）	2（8.7）	6（26.1）	15（65.2）
第2类	59	68.0（12.0）	57（96.6）	34（57.6）	25（42.4）	46（78.0）	13（22.0）	5（8.5）	1（1.7）	15（25.4）	24（40.7）
第3类	25	71.0（12.0）	1（4.0）^ab	11（44.0）	14（56.0）^a	11（44.0）	14（56.0）^ab	6（24.0）	3（12.0）	10（40.0）	12（48.0）
检验统计量值		4.640^c	58.921^d	8.222^d		11.031^d		3.418^d	4.155^d	1.932^d	3.996^d
P值		0.098	<0.001	0.016		0.004		0.181	0.125	0.381	0.136
类别	糖尿病〔n（%）〕	冠心病〔n（%）〕	脑卒中〔n（%）〕	TC（±s，mmol/L）	TG〔M（QR），mmol/L〕	HDL-C〔M（QR），mmol/L〕	LDL-C（±s，mmol/L）	UA〔M（QR），mmol/L〕	Cr〔M（QR），mmol/L〕	FBG〔M（QR），mmol/L〕
第1类	5（21.7）	12（52.2）	14（60.9）	5.31±0.78	1.74（1.04）	1.26（0.54）	3.06±0.74	297.00（97.00）	63.00（19.00）	5.73（1.10）
第2类	4（6.8）	26（44.1）	33（47.1）	5.37±0.92	1.49（1.06）	1.40（0.41）	3.06±0.76	284.00（75.00）	64.00（16.00）	5.42（0.82）
第3类	2（8.0）	9（36.0）	15（60.0）	4.78±1.03^b	1.61（0.94）	1.19（0.35）	2.71±0.74	323.00（66.00）^ab	76.00（22.00）^ab	5.17（0.77）
检验统计量值	2.301^d	1.273^d	0.222^d	3.672^e	1.744^c	7.373^c	2.098^e	7.335^c	8.305^c	4.980^c
P值	0.129	0.529	0.895	0.030	0.418	0.025	0.128	0.026	0.016	0.083

[1]	CHEN Junyu, HAN Guangli, LI Chunhong, CHEN Yidi, YANG Nana, LUO Xiaoxi. Classification Study of Self-management Types among Pregnant Women in Rural Areas Based on Cluster Analysis [J]. Chinese General Practice, 2025, 28(26): 3289-3293.
[2]	ZHENG Chuanlei, DING Ruicong, WANG Qi, GUO Yixing, LI Jian, HUANG Zhengchun, DONG Minghua, LUO Xiaoting, WU Qingfeng. Study on the Relationship between Dietary Patterns and Dyslipidemia among Residents Aged ≥35 Years: Based on the Gannan Chronic Disease Cohort Survey Data [J]. Chinese General Practice, 2024, 27(30): 3739-3745.
[3]	CAO Deli, ZHOU Wei, ZHANG Xiufang, JIANG Lin, BAO Xing, SHEN Qinghua. The Willingness of General Practitioners to Participate in Graded Diagnosis and Treatment Based on the Fusion Model of TPB and TAM [J]. Chinese General Practice, 2024, 27(25): 3108-3114.
[4]	GE Yaxue, DING Zhiguo, CHEN Xiaoheng, LI Huilong, QI Shuo, HU Rui. Study on the Distribution of Clinical Symptoms and Syndromes of Hashimoto's Thyroiditis Complicated with Thyrotoxicosis [J]. Chinese General Practice, 2024, 27(21): 2630-2638.
[5]	WANG Fei, TANG Jingqi, SUN Xiaonan, SUN Xinying, LI Jun, MENG Xingxing, WU Yibo. Design and Development of Scales in Primary Care: Practical Steps and Statistical Methods [J]. Chinese General Practice, 2024, 27(13): 1573-1583.
[6]	PENG Yan, LIU Xiao, ZHANG Jinying, LI Jie, DENG Menghui, LI Yuanyuan, YANG Yanni. Types of Knowledge, Beliefs, Behaviors of Reducing Dementia Risk in Middle-aged and Elderly Adults in Community and Difference Analysis of Cognitive Function [J]. Chinese General Practice, 2024, 27(05): 521-526.
[7]	GU Jingmei, JI Shuyu, XI Qian, PENG Houxuan, QIN Li, ZHAO Can, CHEN Peimeng, HUANG Xiaocui, LIANG Ruiying, SHEN Ying. Validity and Reliability of an Evaluation Index System for Consultation Competency of General Practitioners Practicing in Primary Care Settings [J]. Chinese General Practice, 2024, 27(04): 476-484.
[8]	XIA Yuxian, FU Lijuan, LUO Wenyi, SHEN Xiaoyi, NI Ping. Family Management Styles and Associated Factors for Children after Palliative Surgery for Complex Congenital Heart Disease [J]. Chinese General Practice, 2023, 26(16): 1995-2003.
[9]	WANG Jun, ZHANG Dong, FENG Zhenzhen, ZHANG Shujuan, ZHAO Guixiang, ZHANG Hailong, LI Jiansheng. Literature-based Research on Common Syndromes of Cough Variant Asthma [J]. Chinese General Practice, 2023, 26(03): 321-328.
[10]	Chenxi ZHANG, Shengnan GUAN, Kai XIE, Kang ZHANG, Haifeng WANG. Distribution of Clinical Traditional Chinese Medicine Syndromes in Patients with Severe Community-acquired Pneumonia [J]. Chinese General Practice, 2022, 25(21): 2640-2645.
[11]	WAN Jiamin，ZHAO Kexiang，SUN Yue，DENG Zidan，LUO Cheng，XIAO Qian. Advances in the Relationship between Osteosarcopenic Obesity Syndrome and Type 2 Diabetes　 [J]. Chinese General Practice, 2021, 24(9): 1152-1157.
[12]	XIAO Yuanyuan，JIANG Zhifeng，JIANG Fusong，ZHU Chaoyu，GAO Qingge，WEI Li. Correlation of Serum Uric Acid Level and Body Fat Distribution in Individuals by Glucose Metabolism　 [J]. Chinese General Practice, 2021, 24(21): 2675-2679.
[13]	YU Menggen，ZHAO Xuan，LI Huiwen，YU Yahang，YUAN Beibei，MENG Qingyue. Perception and Evaluation of Integrated Medical and Preventive Services among Primary Care Doctors and Nurses in China　 [J]. Chinese General Practice, 2021, 24(1): 40-45.
[14]	ZHENG Shuping，YUAN Hong. Analysis of Risk Factors Related to Type 2 Diabetic Kidney Disease based on Physical Examination　 [J]. Chinese General Practice, 2020, 23(6): 707-711.
[15]	XUE Mengting，JIANG Rongrong，HUANG Anle，BU Zihan，LI Qingyun，XU Guihua. Analysis and Prospect of Hot Topics in Mild Cognitive Impairment Research in China　 [J]. Chinese General Practice, 2020, 23(27): 3381-3388.