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Article(id=1324050905787671492, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324050876230415089, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202602164, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1770998400000, receivedDateStr=2026-02-14, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793607935616, onlineDateStr=2026-11-02, pubDate=1781020800000, pubDateStr=2026-06-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793607935616, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793607935616, creator=13701087609, updateTime=1793607935616, updator=13701087609, issue=Issue{id=1324050876230415089, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='11', pageStart='1921', pageEnd='2112', issueExtLink='null', onlineDate='null', pubDate='1781020800000', pubDateStr='2026-06-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793607928569, creator='13701087609', updateTime=1793670049253, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324311429322507008, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324050876230415089, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324311429322507009, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324050876230415089, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1987, endPage=1993, ext={EN=ArticleExt(id=1324050906060301253, articleId=1324050905787671492, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Association of combination patterns of nutrient supplements with phenotypic age acceleration: a cross-sectional analysis of the UK Biobank, columnId=1228016572783063333, journalTitle=Modern Preventive Medicine, columnName=Nutrition and Food Hygiene, runingTitle=null, highlight=null, articleAbstract=
Objective

Phenotypic age acceleration is an important risk phenotype for multiple chronic diseases. Nutrient supplements are widely used among middle-aged and older adults, often in combination, as a potential strategy to address age-related health concerns. However, the associations between supplement combinations and phenotypic age acceleration remain insufficiently evaluated. This study aims to examine the associations of supplement use and common combinations with phenotypic age acceleration.

Methods

A cross-sectional analysis was conducted using baseline data from the UK Biobank, including 415 636 participants aged 40-69 years. Multivariable linear regression and restricted cubic spline models were applied to assess the associations between supplement use and phenotypic age acceleration.

Results

Regular supplement users accounted for 50.6% of participants, and had lower phenotypic age acceleration than non-users (β=-0.18 years, 95% CI: -0.21~-0.15). Phenotypic age acceleration decreased non-linearly with increasing number of supplements. Use of glucosamine, fish oil, or multivitamins alone was associated with lower phenotypic age acceleration, with the strongest association observed for glucosamine (β=-0.39 years, 95% CI: -0.47~-0.30). The association was stronger for the combination of glucosamine and fish oil (β=-0.60 years, 95% CI: -0.69~-0.52). Adding vitamin C or calcium to this combination also showed negative associations, whereas combinations containing zinc showed no clear association.

Conclusion

Regular nutrient supplement use was associated with lower phenotypic age acceleration, with a non-linear decrease as the number of supplements increased. Associations varied across combinations, and those containing glucosamine and fish oil showed more consistent associations.

, authors=Li-jun LIANG1, Jia-qi HUANG1, Zhen-yu GAO1, Xiong XIAO1, 2, authorsList=Li-jun LIANG, Jia-qi HUANG, Zhen-yu GAO, Xiong XIAO, authorCompany=null, correspAuthors=Xiong XIAO, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1324050907134043084, articleId=1324050905787671492, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=营养补剂联合服用模式与表型年龄加速的关联——基于英国生物样本库的横断面分析, columnId=1228016573034721597, journalTitle=现代预防医学, columnName=营养与食品卫生, runingTitle=null, highlight=null, articleAbstract=
目的

表型年龄加速是多种慢性疾病的重要风险表型。营养补剂作为应对年龄相关健康问题的潜在策略，在中老年人群中广泛使用且常联合摄入，但其组合服用与衰老相关表型的关联尚缺乏系统评估。本研究旨在探讨补剂使用及常见组合与表型年龄加速的关联。

方法

基于英国生物样本库基线数据开展横断面分析，纳入415 636名40～69岁对象，采用多元线性回归和限制性立方样条分析补剂使用与表型年龄加速的关联。

结果

规律服用者占50.6%，其表型年龄加速低于未服用者（β=-0.18年，95%CI：-0.21～-0.15）。服用种类数增加时，表型年龄加速呈非线性下降。单用氨糖、鱼油或复合维生素均与较低表型年龄加速相关，其中氨糖关联最强（β=-0.39年，95%CI：-0.47～-0.30）。氨糖联合鱼油的关联进一步增强（β=-0.60年，95%CI：-0.69～-0.52），在此基础上叠加维生素C或钙亦呈负相关，而含锌组合的关联不明确。

结论

补剂规律服用与表型年龄加速降低相关，且随种类数增加呈非线性下降。不同组合关联强度存在差异，其中含氨糖和鱼油的组合关联较为稳定。

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梁丽君（2000—），女，硕士在读，研究方向：流行病与卫生统计学

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肖雄， E-mail：
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Basic characteristics of study participants ［M（P25，P75），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
人群特征全人群(n=415 636)补剂服用状态
服用(n=210 502)未服用(n=205 134)
表型年龄52.78 (45.19, 59.36)53.81 (46.62, 59.85)51.54 (43.89, 58.73)
PAA-5.29 (-8.18, -1.98)-5.63 (-8.49, -2.35)-4.96 (-7.84, -1.62)
年龄（岁）58.33 (50.58, 63.67)59.92 (52.58, 64.5)56.42 (48.92, 62.67)
性别（女）223 472 (53.80)124 529 (59.2)98 943 (48.2)
种族［白人（多数）］394 001 (94.80)199 111 (94.6)194 890 (95)
教育
初中及以下72 181 (17.40)36 660 (17.4)35 521 (17.30)
高中207 887 (50.00)106 151 (50.4)101 736 (49.60)
大学及以上135 568 (32.60)67 691 (32.2)67 877 (33.10)
TDI-2.15 (-3.65, 0.51)-2.24 (-3.69, 0.33)-2.06 (-3.6, 0.67)
职业状况
就业256 939 (61.80)119 177 (56.60)137 762 (67.20)
未就业7 478 (1.80)3 120 (1.50)4 358 (2.10)
退休147 361 (35.50)86 217 (41.00)61 144 (29.80)
月经状况（已绝经）135 724 (32.7)81 665 (38.8)54 059 (26.4)
吸烟（是）44 005 (10.60)18 704 (8.9)25 301 (12.3)
饮酒（是）382 482 (92.00)193 637 (92)188 845 (92.1)
饮料（是）385 432 (92.70)194 637 (92.5)190 795 (93)
饮茶（是）354 518 (85.30)182 234 (86.6)172 284 (84)
健康膳食224 734 (54.10)126 892 (60.3)97 842 (47.7)
体力活动 (MET·h/week)4.33 (1.96, 8.76)4.56 (2.13, 9.08)4.08 (1.82, 8.4)
失眠（是）314 968 (75.80)163 384 (77.6)151 584 (73.9)
抑郁症状（是）25 600 (6.20)12 376 (5.9)13 224 (6.4)
焦虑症状（是）19 996 (4.80)9 836 (4.7)10 160 (5)
BMI (kg/m2)26.75 (24.16, 29.9)26.52 (23.99, 29.6)26.99 (24.34, 30.2)
自报疾病
癌症31 487 (7.6)17 233 (8.2)14 254 (6.9)
心血管疾病123 951 (29.8)63 216 (30)60 735 (29.6)
糖尿病21 578 (5.2)10 059 (4.8)11 519 (5.6)
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研究对象基本特征［M（P25，P75），n（%）］

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人群特征全人群(n=415 636)补剂服用状态
服用(n=210 502)未服用(n=205 134)
表型年龄52.78 (45.19, 59.36)53.81 (46.62, 59.85)51.54 (43.89, 58.73)
PAA-5.29 (-8.18, -1.98)-5.63 (-8.49, -2.35)-4.96 (-7.84, -1.62)
年龄（岁）58.33 (50.58, 63.67)59.92 (52.58, 64.5)56.42 (48.92, 62.67)
性别（女）223 472 (53.80)124 529 (59.2)98 943 (48.2)
种族［白人（多数）］394 001 (94.80)199 111 (94.6)194 890 (95)
教育
初中及以下72 181 (17.40)36 660 (17.4)35 521 (17.30)
高中207 887 (50.00)106 151 (50.4)101 736 (49.60)
大学及以上135 568 (32.60)67 691 (32.2)67 877 (33.10)
TDI-2.15 (-3.65, 0.51)-2.24 (-3.69, 0.33)-2.06 (-3.6, 0.67)
职业状况
就业256 939 (61.80)119 177 (56.60)137 762 (67.20)
未就业7 478 (1.80)3 120 (1.50)4 358 (2.10)
退休147 361 (35.50)86 217 (41.00)61 144 (29.80)
月经状况（已绝经）135 724 (32.7)81 665 (38.8)54 059 (26.4)
吸烟（是）44 005 (10.60)18 704 (8.9)25 301 (12.3)
饮酒（是）382 482 (92.00)193 637 (92)188 845 (92.1)
饮料（是）385 432 (92.70)194 637 (92.5)190 795 (93)
饮茶（是）354 518 (85.30)182 234 (86.6)172 284 (84)
健康膳食224 734 (54.10)126 892 (60.3)97 842 (47.7)
体力活动 (MET·h/week)4.33 (1.96, 8.76)4.56 (2.13, 9.08)4.08 (1.82, 8.4)
失眠（是）314 968 (75.80)163 384 (77.6)151 584 (73.9)
抑郁症状（是）25 600 (6.20)12 376 (5.9)13 224 (6.4)
焦虑症状（是）19 996 (4.80)9 836 (4.7)10 160 (5)
BMI (kg/m2)26.75 (24.16, 29.9)26.52 (23.99, 29.6)26.99 (24.34, 30.2)
自报疾病
癌症31 487 (7.6)17 233 (8.2)14 254 (6.9)
心血管疾病123 951 (29.8)63 216 (30)60 735 (29.6)
糖尿病21 578 (5.2)10 059 (4.8)11 519 (5.6)
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Distribution of nutrient supplement combinations stratified by the number of supplements used ［n（%）］

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服用种类数（层内总n，占全样本%）组合编号组合构成本组合n（层内%）
1种(82 889, 19.94%)A1鱼油34 709 (38.61)
A2复合维生素17 021 (20.53)
A3氨糖14 496 (17.49)
2种(65 600, 15.78)B1鱼油+复合维生素18 452 (28.13)
B2鱼油+氨糖17 215 (26.24)
B3复合维生素+氨糖6 432 (9.80)
3种(32 729, 7.87)C1鱼油+复合维生素+氨糖11 649 (35.59)
C2鱼油+氨糖+维生素C1 809 (5.53)
C3鱼油+复合维生素+维生素C1 723 (5.26)
4种(13 740, 3.31)D1鱼油+复合维生素+氨糖+钙1 525 (11.10)
D2鱼油+复合维生素+氨糖+维生素C1 269 (9.24)
D3鱼油+复合维生素+氨糖+锌550 (4.00)
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按服用种类数分层的营养补剂组合分布［n（%）］

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服用种类数（层内总n，占全样本%）组合编号组合构成本组合n（层内%）
1种(82 889, 19.94%)A1鱼油34 709 (38.61)
A2复合维生素17 021 (20.53)
A3氨糖14 496 (17.49)
2种(65 600, 15.78)B1鱼油+复合维生素18 452 (28.13)
B2鱼油+氨糖17 215 (26.24)
B3复合维生素+氨糖6 432 (9.80)
3种(32 729, 7.87)C1鱼油+复合维生素+氨糖11 649 (35.59)
C2鱼油+氨糖+维生素C1 809 (5.53)
C3鱼油+复合维生素+维生素C1 723 (5.26)
4种(13 740, 3.31)D1鱼油+复合维生素+氨糖+钙1 525 (11.10)
D2鱼油+复合维生素+氨糖+维生素C1 269 (9.24)
D3鱼油+复合维生素+氨糖+锌550 (4.00)
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营养补剂联合服用模式与表型年龄加速的关联——基于英国生物样本库的横断面分析
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梁丽君 1 , 黄佳琪 1 , 高振宇 1 , 肖雄 1, 2
现代预防医学 | 营养与食品卫生 2026,53(11): 1987-1993
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现代预防医学 |营养与食品卫生 2026 , 53 (11) : 1987 -1993
营养补剂联合服用模式与表型年龄加速的关联——基于英国生物样本库的横断面分析
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梁丽君1, 黄佳琪1, 高振宇1, 肖雄1, 2
作者信息
  • 1.四川大学华西公共卫生学院/华西第四医院流行病与卫生统计学系，四川 成都 610041
  • 2.主动健康与食品营养安全四川省重点实验室，四川 成都 610041
通讯作者:
肖雄， E-mail：
作者简介:

梁丽君（2000—），女，硕士在读，研究方向：流行病与卫生统计学

Association of combination patterns of nutrient supplements with phenotypic age acceleration: a cross-sectional analysis of the UK Biobank
Li-jun LIANG1, Jia-qi HUANG1, Zhen-yu GAO1, Xiong XIAO1, 2
Affiliations
  • Department of Epidemiology and Health Statistics, West China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
出版时间: 2026-06-10 doi: 10.20043/j.cnki.MPM.202602164
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摘要
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目的

表型年龄加速是多种慢性疾病的重要风险表型。营养补剂作为应对年龄相关健康问题的潜在策略，在中老年人群中广泛使用且常联合摄入，但其组合服用与衰老相关表型的关联尚缺乏系统评估。本研究旨在探讨补剂使用及常见组合与表型年龄加速的关联。

方法

基于英国生物样本库基线数据开展横断面分析，纳入415 636名40～69岁对象，采用多元线性回归和限制性立方样条分析补剂使用与表型年龄加速的关联。

结果

规律服用者占50.6%，其表型年龄加速低于未服用者（β=-0.18年，95%CI：-0.21～-0.15）。服用种类数增加时，表型年龄加速呈非线性下降。单用氨糖、鱼油或复合维生素均与较低表型年龄加速相关，其中氨糖关联最强（β=-0.39年，95%CI：-0.47～-0.30）。氨糖联合鱼油的关联进一步增强（β=-0.60年，95%CI：-0.69～-0.52），在此基础上叠加维生素C或钙亦呈负相关，而含锌组合的关联不明确。

结论

补剂规律服用与表型年龄加速降低相关，且随种类数增加呈非线性下降。不同组合关联强度存在差异，其中含氨糖和鱼油的组合关联较为稳定。

营养补剂  /  表型年龄加速  /  生物学衰老  /  限制性立方样条
Objective

Phenotypic age acceleration is an important risk phenotype for multiple chronic diseases. Nutrient supplements are widely used among middle-aged and older adults, often in combination, as a potential strategy to address age-related health concerns. However, the associations between supplement combinations and phenotypic age acceleration remain insufficiently evaluated. This study aims to examine the associations of supplement use and common combinations with phenotypic age acceleration.

Methods

A cross-sectional analysis was conducted using baseline data from the UK Biobank, including 415 636 participants aged 40-69 years. Multivariable linear regression and restricted cubic spline models were applied to assess the associations between supplement use and phenotypic age acceleration.

Results

Regular supplement users accounted for 50.6% of participants, and had lower phenotypic age acceleration than non-users (β=-0.18 years, 95% CI: -0.21~-0.15). Phenotypic age acceleration decreased non-linearly with increasing number of supplements. Use of glucosamine, fish oil, or multivitamins alone was associated with lower phenotypic age acceleration, with the strongest association observed for glucosamine (β=-0.39 years, 95% CI: -0.47~-0.30). The association was stronger for the combination of glucosamine and fish oil (β=-0.60 years, 95% CI: -0.69~-0.52). Adding vitamin C or calcium to this combination also showed negative associations, whereas combinations containing zinc showed no clear association.

Conclusion

Regular nutrient supplement use was associated with lower phenotypic age acceleration, with a non-linear decrease as the number of supplements increased. Associations varied across combinations, and those containing glucosamine and fish oil showed more consistent associations.

Nutrient supplements  /  Phenotypic age acceleration  /  Biological aging  /  Restricted cubic spline
梁丽君, 黄佳琪, 高振宇, 肖雄. 营养补剂联合服用模式与表型年龄加速的关联——基于英国生物样本库的横断面分析. 现代预防医学, 2026 , 53 (11) : 1987 -1993 . DOI: 10.20043/j.cnki.MPM.202602164
Li-jun LIANG, Jia-qi HUANG, Zhen-yu GAO, Xiong XIAO. Association of combination patterns of nutrient supplements with phenotypic age acceleration: a cross-sectional analysis of the UK Biobank[J]. Modern Preventive Medicine, 2026 , 53 (11) : 1987 -1993 . DOI: 10.20043/j.cnki.MPM.202602164
正文
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人口老龄化加速使慢性非传染性疾病负担持续上升，延长健康寿命已成为公共卫生与临床预防的重要目标[1]。作为膳食之外的重要营养来源，营养补剂因获取便利，在中老年人群中被广泛使用。美国国家营养健康调查（National Health and Nutrition Examination Survey，NHANES）报告显示，2017—2018年成人营养补剂使用率达57.6%，且随年龄增长而升高，多种补剂并用也随年龄递增[2]。英国人群补剂使用亦较普遍，且常呈多种联合与类型聚集特征[3]。营养补剂已成为老龄人群健康管理中的常见暴露，具有研究价值。既往研究表明，部分补剂可能与衰老相关过程及健康结局有关，如复合维生素与较长端粒相关[4]，鱼油所含ω-3脂肪酸可能通过炎症与氧化应激通路影响衰老相关生理失衡[5]，氨基葡萄糖使用与较低全因及心血管死亡风险相关[6]。人群研究还显示，ω-3脂肪酸摄入与表型年龄加速可能存在剂量-反应关系[7]。这些研究为补剂可能影响衰老相关结局提供了依据，但仍不足以清晰回答真实人群中补剂使用与衰老表型之间的关系，因为相关研究多聚焦单一成分或少数特定补剂，难以反映普遍存在的联合使用特征及其整体效应。此外，补剂使用行为还常与社会经济状况、健康意识及生活方式共变[8-9]，易产生健康使用者效应等偏倚[10]。相比单一成分研究，从组合模式角度考察补剂使用更具必要性和现实意义。现实生活中，补剂常表现为多种成分并用和类型聚集，不同成分间可能存在协同或拮抗作用[11-12]，其综合效应未必等同于单一补剂效应的简单叠加。同时，联合补充也可能带来潜在风险，如补剂来源的高钙摄入与癌症死亡风险升高相关[13]。因此，仅考察单一补剂难以全面揭示其与衰老相关结局的真实关系，也难为合理补充提供依据。
衰老评估方面，时间年龄难以反映个体间真实生理差异[14]，生物学年龄相关指标可从多维度刻画机体衰老状态[15]。基于常规生物标志物构建的表型年龄（phenotypic age）可整合多项血液学与生化指标，以年为单位量化个体衰老水平。（phenotypic age acceleration，PAA）用于表征个体相对同龄人的衰老偏离，已被证实与发病及死亡风险相关[16-18]。由于表型年龄所需指标多来自常规检测，其可及性与可推广性较强，适用于大样本人群研究与风险分层[19]。基于此，本研究利用英国生物样本库（UK Biobank，UKB）基线数据[20]，在总体使用与服用种类数表征基础上，进一步探讨常见联合服用组合与PAA的关联，并结合限制性立方样条（restricted cubic spline, RCS）评估剂量-反应关系的非线性特征，为不同补剂服用模式与表型衰老的关联差异提供人群证据。
1　对象与方法
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1.1　研究对象
UKB是英国大型前瞻性人群队列[21]，2006—2010年在英国多地区招募了50余万名40～69岁人群，收集社会人口学特征、生活方式及健康状况等信息，并通过触屏问卷、体格检查及生物样本采集获取基线资料。研究获英国西北多中心研究伦理委员会批准（11/NW/0382），参与者均签署知情同意。本研究基于 UKB 基线数据，排除缺乏用于构建表型年龄加速的生化指标完整数据参与者86 002人及营养补剂信息缺失711人，最终纳入415 636人。
1.2　营养补剂服用状态、种类数和组合
UKB基线通过问卷条目收集营养补剂规律服用信息，研究对象分别在维生素类和其他补剂列表中多选作答（对应UKB字段编号6 155和6 179[22-23]），“无上述”记为未服用，“拒答”记为缺失。本研究据此提取13种营养补剂条目，包括维生素A、B、C、D、E、B9、复合维生素（可含矿物质，简称复合维生素）、鱼油（包括鳕鱼肝油，简称鱼油）、氨基葡萄糖（简称氨糖）、钙、锌、铁和硒，并分别构建0/1指示变量。在此基础上计算个体服用种类数（0～13种），进一步按0、1～2、3～4、5～6、7～8和9+分组，用于表征补剂服用强度，同时根据是否至少服用1种补剂定义服用状态。联合服用模式基于13种补剂指示变量构建，并按服用种类数分层，在服用1、2、3和4种补剂者中分别统计各层的分布频数，选取频数居前的3种组合作为常见组合，其余合并为“其他”，未服用者单列，用于描述性统计及后续关联分析。每名研究对象仅按其实际服用的完整组合归入一个组合类别，各组合之间相互排斥。由于服用5种及以上补剂者组合形式较多且频数较低，故未纳入组合分析，仅在种类数分析中评估。
1.3　表型年龄加速的评估
本研究采用既往提出并广泛应用的表型年龄方法学框架，基于基线实际年龄及9项生物标志物计算个体表型年龄，包括白蛋白、肌酐、血糖、C反应蛋白［经对数转换，按log（CRP/10）处理］、淋巴细胞百分比、平均红细胞体积、红细胞分布宽度、碱性磷酸酶和白细胞计数。表型年龄的计算流程与参数取值详见既往文献[16-17]。PAA采用回归残差法定义，即以表型年龄对实际年龄进行线性回归，所得残差表示个体表型年龄相对同龄预期水平的偏离，残差为正表示衰老加速，负值表示相对更年轻。
1.4　协变量
协变量基于既往文献证据并结合有向无环图（DAG）确定[24]，用于控制潜在混杂，包括社会人口学因素、生活方式因素和健康相关因素，具体为年龄、性别、种族、教育程度、汤森剥夺指数（Townsend depivation index,TDI，反映居住地区的社会经济剥夺程度，数值越高表示社会经济状况越差）、职业状况和绝经状态、吸烟、饮酒、饮料与饮茶摄入、健康膳食和非久坐体力活动水平、失眠、抑郁与焦虑症状、身体质量指数（body mass index,BMI）及自报癌症、心血管疾病和糖尿病。其中，健康膳食根据食物频率问卷中水果、蔬菜、鱼、加工肉类、未加工红肉、全谷物和精制谷物7类膳食成分构建评分，非久坐体力活动水平根据步行、中高强度活动的每周MET值加总后换算为MET·h/周。
1.5　统计分析
研究对象基线特征按营养补剂是否规律服用分组描述，连续变量以中位数（四分位数间距）表示，并在回归分析中按连续变量纳入模型，模型拟合情况结合残差诊断评估；分类变量以例数和百分比表示。协变量缺失采用多重插补处理，插补后数据用于回归分析。
为系统刻画补剂使用行为，本研究按“是否服用-服用数量-联合组合”三个层级开展分析。以PAA为因变量，分别将补剂服用状态、服用种类数分组及常见组合变量纳入多元线性回归模型，估计β值及95%置信区间（confidence interval, CI），并按社会人口学因素、生活方式因素及健康相关因素逐步调整混杂因素。服用种类数分组变量用于趋势检验，同时将服用种类数作为连续变量，采用RCS（4个结点）探讨其与PAA的非线性关联，并绘制剂量-反应曲线及95%CI。服用组合分析以未服用者为参照，评估不同常见组合与PAA的关联。双侧检验，检验水准α=0.05。统计分析在R 4.5.2软件中完成。
2　结果
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2.1　研究对象特征
本研究共纳入415 636名研究对象，其中规律服用营养补剂者210 502人（50.6%）。全人群表型年龄M（P25，P75）为52.78的（45.19，59.36）岁，PAA的M（P25，P75）为-5.29（-8.18，-1.98）年。与未服用者相比，服用者年龄更大，女性、退休及绝经者比例更高，TDI更低，且总体生活方式更健康，其癌症和糖尿病自报比例略高，心血管疾病比例相近。见表1。
2.2　营养补剂服用状态与PAA的关联分析
核密度分布图显示，服用组PAA分布较未服用组整体向低值方向偏移，见图1A。多因素线性回归结合边际均值估计结果显示，调整协变量后，服用组PAA均值仍低于未服用组，调整后均值差为-0.18年（95%CI：-0.22～-0.15，P<0.001），提示规律服用营养补剂与较低PAA独立相关，见图1B。
2.3　营养补剂服用种类数与PAA的剂量-反应关系
以未服用者为参考，多因素调整后，PAA随营养补剂服用种类数增加而降低，趋势检验显示，每增加1种补剂，PAA平均降低0.13年（95%CI：-0.15～-0.11）。与未服用组相比，服用1～2种者PAA已降低，3～4种者降幅进一步扩大，约于3种后，下降趋势逐渐减缓，5种后进一步趋于平缓。进一步将服用种类数作为连续变量纳入RCS模型，总体关联及非线性检验均具有统计学意义（总体：F=258.85，P<0.001；非线性：F=81.98，P<0.001），见图2B。剂量-反应曲线呈“先陡后缓”的下降形态，即在低服用种类范围内下降更为明显，随后降幅逐渐减小。高值区间95%CI增宽，与该区间样本较少所致的不确定性增加一致。
2.4　营养补剂服用组合与PAA的关联分析
为描述一般人群中营养补剂的联合使用特征，本研究按基线补剂服用种类数分层（1～4种），统计各层内频数居前的三种常见组合及其构成比，结果见表2，并据此开展后续关联分析。
以未服用者为参考，多因素调整后，多数常见补剂服用组合与较低PAA相关，见图3。单一补剂中，氨糖（β=-0.39，95%CI：-0.47～-0.30）、鱼油（β=-0.35，95%CI：-0.41～-0.30）及复合维生素（β=-0.24，95%CI：-0.32～-0.16）均与PAA降低相关。两种组合中，鱼油+氨糖关联最强（β=-0.60，95%CI：-0.69～-0.52），其次为复合维生素+氨糖。三种组合中，鱼油+复合维生素+氨糖与PAA降低相关（β=-0.69，95%CI：-0.78～-0.59），鱼油+氨糖+维生素C结果相近。四种组合中，复合维生素+鱼油+氨糖+钙（β=-0.70，95%CI：-0.96～-0.44）及维生素C+复合维生素+鱼油+氨糖（β=-0.71，95%CI：-1.00～-0.43）亦与PAA降低相关。而复合维生素+鱼油+氨糖+锌的关联无统计学意义，考虑到该组合样本量较小（n=550）且95%CI较宽，结果存在一定不确定性，不宜简单解释为无效或负向关联。
3　讨论
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本研究基于UKB基线超41万名人群数据，从服用状态、服用种类数及具体组合模式三个维度系统评估了营养补充剂使用与PAA的关联。结果显示，规律服用营养补剂与较低PAA独立相关；服用种类数与PAA呈“先陡后缓”的非线性负相关；常见组合中，以氨糖、鱼油和复合维生素的组合多与PAA降低相关，其中氨糖联合鱼油，并进一步叠加复合维生素或维生素C的组合关联更强。这提示在普通中老年人群中，适度且目标明确的补剂使用可能比泛化补充更具实际健康效益。
既往研究显示，营养补剂多与衰老相关指标改善呈较为一致的有利关联。已有证据表明，复合维生素使用者端粒长度可能更长[4]，同时，鱼油中的ω-3脂肪酸及抗氧化营养素可能通过炎症与氧化应激相关途径参与衰老过程调节[5]。本研究所用的PAA指标整合了9项临床生物标志物，反映多系统生理功能失调程度，与死亡及多种慢性病风险密切相关[17]。因此，补剂使用与较低PAA的关联，可能反映其对减缓多系统生理功能衰退的潜在益处。
本研究创新性地在人群层面揭示了营养补剂服用种类数与PAA之间“先陡后缓”的非线性关系，即在较低服用种类范围内下降较明显，约于3种后开始减缓，5种后进一步趋于平缓。既往研究亦提示，营养暴露与衰老指标可能存在阈值或非线性特征，如ω-3脂肪酸摄入与PAA呈负向关系并存在阈值效应[7]。高种类服用者曲线趋于平缓且置信区间增宽，可能反映边际效益递减、人群异质性增强及残余混杂的影响。同时，多种营养素叠加亦可能带来相互作用与潜在风险，有研究提示补剂来源的高钙摄入与癌症死亡风险升高相关[13]。总体而言，该结果提示补剂使用在衰老相关表型上的收益更可能呈非线性特征，强调适度与针对性补充的重要性。
营养补剂服用组合分析提示，不同成分与PAA关联存在异质性，且部分组合可能具有叠加效应。含氨糖、鱼油和复合维生素的常见组合总体关联更强，其中氨糖与鱼油联用及进一步叠加复合维生素或维生素C时关联更强。既往研究指出，氨糖使用与较低全因及心血管死亡风险相关[6]，并可能通过影响长寿相关代谢通路及减轻低度炎症发挥作用[25-26]。DO-HEALTH试验事后分析也显示，ω-3干预可轻度减缓多种DNA甲基化衰老时钟，并在特定时钟上与维生素D及运动呈现叠加效应[27]。本研究结果与多通路互补可能带来叠加效应的证据方向一致。对于复合维生素+鱼油+氨基葡萄糖+锌组合，由于样本量较小（n=550）且置信区间较宽，当前结果存在一定不确定性，不能简单解读为无效或负向作用[28]，同时，也不能排除复杂组合中成分间相互作用差异对关联强度的影响[12,29]。
本研究亦存在一定局限性。首先，基线横断面分析难以明确时间顺序与因果方向，反向因果不能排除；个体因健康担忧、慢性不适或关节症状而开始使用补剂的动机，也可能同时影响补充剂选择与PAA，导致残余混杂。受UKB重复评估中相关生物标志物覆盖有限，本研究未能进一步构建可比的纵向表型年龄指标。其次，补剂暴露来自自报问卷，缺乏剂量、品牌、服用时长、依从性及使用原因等信息，可能导致错分并限制混杂控制。再次，尽管采用多重插补并调整社会经济、生活方式、饮食相关因素及慢性病状况，仍不能排除未测量或测量不充分的混杂。最后，UKB参与者具有健康志愿者特征，且以中老年白人为主，结果外推需谨慎。
综上，本研究基于UKB大样本人群数据，系统评估了营养补剂使用与PAA的关联，发现规律的服用与较低PAA相关，服用种类数与PAA呈非线性下降关系，含氨糖、鱼油和复合维生素的常见组合多与PAA降低相关，但不同组合间存在差异。鉴于本研究为基于国外队列的观察性分析，上述发现主要提示相关性，不同营养补剂联合使用模式与PAA的关联强度存在差异，相关发现仍需在我国人群中进一步验证。
基金
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  • 国家自然科学基金面上项目(82574204)
文献
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2026年第53卷第11期
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doi: 10.20043/j.cnki.MPM.202602164
  • 接收时间：2026-02-14
  • 首发时间：2026-11-02
  • 出版时间：2026-06-10
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  • 收稿日期：2026-02-14
基金
国家自然科学基金面上项目(82574204)
作者信息
    1.四川大学华西公共卫生学院/华西第四医院流行病与卫生统计学系，四川 成都 610041
    2.主动健康与食品营养安全四川省重点实验室，四川 成都 610041

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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