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URL: https://doi.org/10.3390/nu15092080
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Publisher: Pu et al. · Nutrients · research systematic review
Source date: 2023
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NutrientsNutrients1692nutrients101521595nutrientsNutrients2072-6643Multidisciplinary Digital Publishing Institute  (MDPI)PMC10180699PMC10180699.110180699101806993743218010.3390/nu15092080nutrients-15-020801Systematic ReviewEffects of Oral Collagen for Skin Anti-Aging: A Systematic Review and Meta-AnalysisPuSzu-Yu1†HuangYa-Li2†PuChi-Ming34KangYi-No5678https://orcid.org/0009-0001-9022-4029HoangKhanh Dinh9https://orcid.org/0000-0001-9772-482XChenKee-Hsin51011121314‡https://orcid.org/0000-0002-1595-6553ChenChiehfeng25615*‡GiampieriFrancescaAcademic Editor1School of Medicine, College of Medicine, Taipei Medical University, Taipei City 110, Taiwan; b101110047@tmu.edu.tw2Department of Public Health, School of Medicine, College of Medicine, Taipei Medical University, Taipei City 11031, Taiwan; ylhuang@tmu.edu.tw3Division of Plastic Surgery, Department of Surgery, Cathay General Hospital, Taipei City 106, Taiwan; pkman9335@msn.com4School of Medicine, College of Life Science and Medicine, National Tsing Hua University, Hsinchu City 300, Taiwan5Cochrane Taiwan, Taipei Medical University, Taipei City 110, Taiwan; kynacad@gmail.com (Y.-N.K.); keehsin@tmu.edu.tw (K.-H.C.)6Evidence-Based Medicine Center, Wan Fang Hospital, Taipei Medical University, Taipei City 116, Taiwan7Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei City 116079, Taiwan8Institute of Health Policy and Management, College of Public Health, National Taiwan University, Taipei City 100, Taiwan9Department of Histopathology, Hai Phong University of Medicine and Pharmacy, Hai Phong 04254, Vietnam; hdkhanh@hpmu.edu.vn10Post-Baccalaureate Program in Nursing, College of Nursing, Taipei Medical University, Taipei City 11031, Taiwan11Department of Nursing, Wan Fang Hospital, Taipei Medical University, Taipei City 11696, Taiwan12Research Center in Nursing Clinical Practice, Wan Fang Hospital, Taipei Medical University, Taipei 11696, Taiwan13Evidence-Based Knowledge Translation Center, Wan Fang Hospital, Taipei Medical University, Taipei City 11696, Taiwan14School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University, Selangor 47500, Malaysia15Division of Plastic Surgery, Department of Surgery, Wan Fang Hospital, Taipei Medical University, Taipei City 116, Taiwan*Correspondence: clifchen@tmu.edu.tw†These authors contributed equally to this work.‡These authors contributed equally to this work.2642023520231594356982080133202307420231842023260420231305202326092025© 2023 by the authors.2023https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).This paper presents a systematic review and meta-analysis of 26 randomized controlled trials (RCTs) involving 1721 patients to assess the effects of hydrolyzed collagen (HC) supplementation on skin hydration and elasticity. The results showed that HC supplementation significantly improved skin hydration (test for overall effect: Z = 4.94, p < 0.00001) and elasticity (test for overall effect: Z = 4.49, p < 0.00001) compared to the placebo group. Subgroup analyses demonstrated that the effects of HC supplementation on skin hydration varied based on the source of collagen and the duration of supplementation. However, there were no significant differences in the effects of different sources (p = 0.21) of collagen or corresponding measurements (p = 0.06) on skin elasticity. The study also identified several biases in the included RCTs. Overall, the findings suggest that HC supplementation can have positive effects on skin health, but further large-scale randomized control trials are necessary to confirm these findings.oral collagenskinanti-agingsystematic reviewmeta-analysisTaipei Municipal Wanfang Hospital (managed by Taipei Medical University)111TMU-WFH-06This research was funded by Taipei Municipal Wanfang Hospital (managed by Taipei Medical University), grant number 111TMU-WFH-06.pmc-status-qastatus0pmc-status-liveyespmc-status-embargonopmc-status-releasedyespmc-prop-open-accessyespmc-prop-olfnopmc-prop-manuscriptnopmc-prop-legally-suppressednopmc-prop-has-pdfyespmc-prop-has-supplementyespmc-prop-pdf-onlynopmc-prop-suppress-copyrightnopmc-prop-is-real-versionnopmc-prop-is-scanned-articlenopmc-prop-preprintnopmc-prop-in-epmcyespmc-license-refCC BY

1. IntroductionThe skin, the largest organ of the body exposed to the external environment, is affected by both intrinsic and extrinsic factors in the aging process [1]. Skin aging is characterized by dehydration, a loss of skin elasticity, and the presence of wrinkles [2]. Skin aging has attracted considerable attention because of the increasingly high beauty standards. Because many countries are becoming aging societies, the psychosocial effects of skin aging increases the need for effective interventions [3]. In this context, the use of nutraceuticals as supplements has increased in recent years [4].Collagen is the main protein structure of various connective tissues, which constitutes 80% of the dry weight of human skin [5]. Collagen is characterized by a triple helix structure formed by the repetition of glycine every third residue, and particularly by proline and hydroxyproline in the other residues [6]. Collagen, the most prevalent component of extracellular matrix, provides mechanical support and directs tissue development [7].Aging induces a decline in the enzymes involved in the post-translational processing of collagen, reducing the number of fibroblasts that synthesize collagen and vessels that supply the skin [8]. The decline in skin quality with age is characterized by a reduction in collagen synthesis and a decrease in skin vascularity, leading to decreased elasticity and the formation of wrinkles [9]. These changes are due to the decline in fibroblast activity and a decrease in the number of blood vessels in the skin [10]. Therefore, the skin undergoes regressive changes with age such as dehydration, a loss of elasticity, and a reduction in epidermal thickness [11]. Various nutrients and supplements are used to improve skin health and maintain a youthful skin appearance [12]. These strategies include topical creams, injectable fillers, and collagen supplements. Topical creams contain collagen as one of the ingredients, and they are designed to enhance skin hydration and firmness [13]. However, topical creams have limited ability to penetrate the skin, which can reduce their effectiveness [13]. Injectable fillers such as hyaluronic acid fillers, stimulate collagen production and provide immediate results by plumping the skin [14]. However, they can be expensive and come with the risk of adverse events such as bruising, swelling, and infection [14]. On the other hand, collagen supplements, particularly those containing hydrolyzed collagen peptides, have been shown to be safe and cost-effective compared to other collagen-based strategies. Furthermore, collagen supplements have the advantage of being taken orally, making them easy to incorporate into daily routines [15].Among these supplements, hydrolyzed collagen (HC) is the most popular and promising skin anti-aging nutraceutical [16]. Other studies have indicated that alanine–hydroxyproline–glycine and serine–hydroxyproline–glycine can be detected in human blood 1 h after the oral ingestion of HC [17,18] and deposited on the skin [19].A recent study demonstrated that HC improves skin hydration and elasticity [16]. Nevertheless, not all sources of HC have the same efficacy. Even at the same dose and duration of administration, some specific sources of collagens are more effective than others [20]. Therefore, studies are required to determine the proper source and therapeutic duration of HC against skin aging.Because an increasing number of clinical studies on collagen supplements have been conducted globally, their results must be summarized in a systematic review and meta-analysis. Therefore, this systematic review and meta-analysis investigated the effects of collagen supplementation on skin hydration and elasticity.2. Materials and Methods2.1. Search Strategy, Inclusion Criteria, and Exclusion CriteriaWe performed a literature search in the Embase, PubMed, and Cochrane Library databases by using the following search terms from Medical Subject Headings with no restrictions applied: (collagen OR hydrolyzed collagen) AND (anti-aging). Relevant studies published before December 2022 were identified. We included studies that met the following criteria: (1) applying a randomized clinical trial (RCT) design; (2) including healthy adults (aged ≥ 18 years); (3) including patients who received HC; (4) being full-text articles written in English. We excluded studies that (1) assessed the combined effect of collagen supplement with another supplement or (2) were RCTs that were not written in English. We extracted raw data from the graphs in articles using WebPlotDigitizer [21].2.2. Data ExtractionTwo independent reviewers (S-YP, CC) extracted the basic information of the included studies. The following types of information were extracted: study meta-data (i.e., first author, publication year, and study design) and information on the study sample (i.e., number of patients, gender, mean age, and baseline characteristics of the treatment and placebo groups), intervention (i.e., the dose of collagen supplement and form), and outcomes (i.e., hydration and elasticity). Continuous outcomes are presented in terms of the mean ± standard deviation (SD), and discrete data are presented in terms of percentage.2.3. Statistical Analysis, Sensitivity Analysis and Bias AssessmentWe used a random-effects model to calculate the SD and mean difference of the identified studies. A p value of <0.05 indicated statistical significance. The levels of heterogeneity among the included studies were determined using Hedge’s I2 tests, and forest plots were generated for each included study. Moreover, I2 ≥ 50% indicated high heterogeneity [22]. The general effect test result was reported as a z-value, which supported the inference of the 95% confidence interval (CI). A sensitivity analysis was performed to negate the effect of potentially influential studies. Each study was classified in accordance with the Cochrane Handbook for Systematic Reviews of Interventions [23]. The Cochrane risk of bias (RoB) 2.0 tool was used to assess the risk of bias in the included RCTs. Five domains of bias were evaluated (selection, performance, detection, attrition, and reporting bias) [24]. In this meta-analysis, all outcomes were analyzed using RevMan software (version 5.4).3. Results3.1. Research Results and Study CharacteristicsFigure 1 shows the flowchart of the literature search process performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines [25]. We identified 1135 studies in our initial search. After removing duplicates and screening titles or abstracts of related articles, we assessed the full-text articles of the remaining 37 studies. Of these studies, 26 articles were included in this systematic review and meta-analysis.3.2. Study CharacteristicsA total of 26 RCTs involving 1721 patients were included in this meta-analysis. The duration of the HC supplementation of the included studies ranged from 2 to 12 weeks. Among the included RCTs, 14 focused on collagens extracted from fish, one focused on collagens extracted from bovine, one focused on collagens extracted from chicken, two focused on collagens extracted from porcine, and nine lacked information regarding the source of collagen. The study characteristics of the included RCTs are presented in Table 1.The measurement of skin hydration levels is commonly conducted using a non-invasive tool called a corneometer. This instrument emits a high-frequency electric current into the skin’s surface and measures the amount of water present in the top layer, expressed in corneometry units. The corneometer is widely used in evaluating the effectiveness of topical products and assessing overall skin health by providing valuable insights into the skin’s moisture barrier. Therefore, it is considered as a valuable tool in measuring the skin hydration levels and assessing the efficacy of skincare products [18,26,27,28,29,30,31,32,33]. On the other hand, the measurement of skin elasticity is often conducted using cutometry, a non-invasive technique that provides valuable insights into skin health. It works by applying a controlled negative pressure to a small area of the skin and measuring the resulting deformation, which is directly proportional to the skin’s elasticity. Cutometry is widely used in research and clinical settings to assess the skin elasticity levels and monitor changes in the skin over time. Overall, it is a safe and reliable tool for evaluating skin health [18,26,27,29,32,33,34,35,36,37].nutrients-15-02080-t001_Table 1Table 1Characteristics of the patients in the included studies.Author (Year)Female/MaleAge RangeTime (Weeks)Intervention (Origin)Outcome ExtractedProksch et al. (2014a) [18]60/035–558, 122.5 g HC/5 g HC (porcine)Elasticity/hydration/trans-epidermal water loss (TEWL)/wrinklesProksch et al. (2014b) [38]107/045–658, 122.5 g collagen peptidesWrinkles/biopsy/procollagen type/elastin/fibrillinYoon et al. (2014) [39]44/0>44123 g HC (fish)Procollagen type 1/fibrillin 1/metalloproteinases 1 and 12/biopsies/immunohistochemical stainingDi Cerbo et al. (2014) [40]30/040–454.5372 mg HCCutaneous pH/hydration/sebum/elasticity/skin tone/elastin/elastase 2/fibronectin/hyaluronic acid/carbonyl proteinsChoi et al. (2014) [32]24/830–4853 g collagen peptidesSkin hydration/elasticity/TEWL/erythema/satisfaction questionnaireSugihara, Inoue, and Wang (2015) [28]53/035–5582.5 g HC (fish)Hydration/elasticity/wrinklesCampos et al. (2015) [29]60/040–501210 g HCCorneal stratum hydration/skin viscoelasticity/dermal echogenicity/high-resolution photographyAsserin et al. (2015) [30]134/040–658, 1210 g HC (porcine)/10 g HC (fish)Skin moisture/TEWL/dermal density/dermal echogenicity/dermal collagen fragmentationInoue, Sugihara, and Wang (2016) [34]80/035–5582.5 g collagen peptidesSkin moisture/elasticity/wrinklesGenovese, Corbo, and Sibilla (2017) [41]111/940–60125 g HCElasticity/biopsies/subjective questionnaireKoizumi et al. (2017) [27]71/030–60123 g collagen peptidesWrinkles/moisture/elasticity/blood tests (γ-glutamyltransferase, mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, mean corpuscular volume, red blood cell, platelet, white blood cell, bilirubin, creatinine, total cholesterol, glucose, hemoglobin, hematocrit, alanine aminotransferase, aspartate aminotransferase, total protein and albumin)Czajka et al. (2018) [42]120/021–70124 g HCElasticity/biopsies/self-perception questionnaireKim (2018) [43]70/040–60121000 mg collagen (fish)Skin hydration/wrinkling/elasticityIto, Seki, and Ueda (2018) [31]17/430–50810 g collagen peptides (fish)Elasticity/moisture/TEWL/skin pH/spots/wrinkle/skin pores/texture/density/collagen score/growth hormone (GH), insulin-like growth factor-1 (IGF-1)Bolke et al. (2019) [26]72/0>3512, 162.5 g collagen peptidesHydration/elasticity/wrinkles/skin density/subjective questionnaire Schwartz et al. (2019) [35]113/036–59120.6 g HC (chicken)Erythema/hydration/TEWL/elasticity/wrinkles/dermal collagen/subjective questionnaireZmitek et al. (2020) [44]31/040–65124 g HC (fish)Dermal density and thickness/viscoelasticity/hydration/TEWL/wrinkles/moisture/dermal microreliefLaing et al. (2020) [45]60/040–70122.5 g collagen peptidesDermal collagen fragmentation/subjective questionnaireSangsuwan and Asawanonda (2020) [37]36/050–604, 85 g HCElasticityNomoto and Iizaka (2020) [33]27/12>65812 g collagen peptidesStratum corneum hydration/elasticityPing (2020) [46]50/035–5085.5 g collagen (fish)Skin hydration/brightness/texture/crow’s feet/collagen contentEvans (2020) [47]50/045–601210 g HC (fish)Wrinkles/elasticity/self-reported appearanceTak (2021) [48]84/040–60121000 mg collagen tripeptidesHydration/elasticity/wrinklesMiyanaga (2021) [49]99/035–50121 g HC/5 g HCSkin water content/TEWL/elasticity/thicknessJung (2021) [50]25/2535–60121000 mg collagen (fish)Skin hydration/TEWL/texture/flexibilityBianchi (2022) [51]52/040–6085 g HCSkin moisturization/elasticity/wrinkle depth3.3. Meta-Analysis Results3.3.1. Pooled Analysis of Selected StudiesSome articles were excluded from the research due to various reasons. Studies conducted by Campos, Czajka, Genovese, and Sangsuwan were not considered as they did not measure the hydration levels, which was a key parameter of interest. Similarly, the Asserin study did not measure elasticity, so its results could not be used to evaluate the impact of elasticity on the outcome measures. The Bianchi and Ping study was excluded due to the lack of standard deviation data for the placebo group, which was necessary for the statistical analysis. The Laing study did not provide sufficient direct data on moisture and elasticity, the primary outcomes of interest, and the provided microscopic observations and questionnaires were insufficient for the research. Finally, the Proksch study did not provide data for the placebo group, making it impossible to compare the results with those of the intervention group. Therefore, these studies did not meet the necessary criteria for inclusion in the research.All included RCTs divided the patients into two groups according to the collagen measurement and skin hydration or elasticity, and then subjected to a meta-analysis. The standard mean difference (SMD) of 18 studies on the effects of HC and the placebo on skin hydration are shown in Figure 2. The overall pooled effect size of 0.63 (95% CI 0.38, 0.88) indicated that HC supplementation significantly improved skin hydration (z = 4.94, p < 0.00001). Figure 3 shows the forest plot of the meta-analysis of 19 studies on the effects of HC on skin elasticity; the results indicate that HC supplementation significantly improved skin elasticity (z = 4.49, p < 0.00001) compared with the placebo group at a pooled effect size of 0.72 (95% CI 0.40, 1.03).3.3.2. Subgroup AnalysisCollagen supplements are available in various forms including gels, liquids, and capsules. The type of collagen used in these supplements can vary depending on the source, with some of the most common types including fish, porcine, chicken, and bovine collagen. A subgroup analysis was performed to determine the effects of multiple sources of HC supplements and duration on skin hydration. The results showed that the supplementation with HC originating from fish, bovine, chicken, porcine, and unknown source significantly improved skin hydration (Figure 4, p < 0.00001). Of these sources, HC originating from chicken had the weakest effect (−0.03, 95% CI −0.40, 0.34) on skin hydration. In addition, we performed subgroup analyses on the duration of HC supplementation for 2, 4, 6, 8, and 12 weeks. The forest plot analysis revealed that the effects of HC supplementation during 4 (p = 0.002), 6 (p = 0.04), 8 (p < 0.00001), and 12 weeks (p = 0.001) significantly differed, as shown in Figure 5. In addition, the effects of the long-term use (>8 weeks) of HC (0.59, 95% CI 0.35, 0.83) were more favorable than that of the short-term use (<8 weeks) of HC (0.39, 95% CI 0.15, 0.63, Figure 6).In addition, three subgroup analyses of the effects of sources of HC, corresponding measurements (R2: Gross elasticity, R5: Net elasticity; elastic portion of relaxation/elastic portion of suction, R7: Elastic portion; elastic portion of relaxation/first maximum amplitude after suction and mm by cutometer) and the duration of HC supplementation on skin elasticity were performed. The subgroup analyses indicated no significant differences in the effects of various sources of HC (p = 0.21, Figure 7) and the corresponding measurements (p = 0.06, Figure 8) on skin elasticity. The subgroup analysis on the duration revealed that 6 weeks of HC supplementation showed no positive effect on skin elasticity (p = 0.05, Figure 9). Furthermore, the effect of the long-term use (>8 weeks) of HC (0.73, 95% CI 0.41, 1.06) was more favorable than that of the short-term use (<8 weeks) of HC (0.67, 95% CI 0.33, 1.00) on skin elasticity. The results of the subgroup analyses are presented in Figure 10.3.4. BiasIn conducting systematic reviews and meta-analyses, it is important to examine the quality of research studies and potential biases. One common method for assessing bias is through the use of RoB (Risk of Bias). RoB evaluates various aspects of a study that could lead to bias such as incomplete outcome data and selective outcome reporting. Each aspect is evaluated based on predefined criteria, and an overall assessment of the study’s risk of bias is made. The goal of RoB is to provide an impartial evaluation of the study’s design, implementation, and reporting to aid in determining the study’s reliability and suitability for inclusion in systematic reviews or meta-analyses [24]. At the study level, we found an RoB in the bias arising from the randomization process in one study [33], bias due to deviations from intended intervention in seven studies [27,30,31,33,35,44,48], bias due to missing outcome data in thirteen studies [18,27,28,30,31,33,34,35,37,44,47,48,51], and bias in the selection of the reported results in two studies [18,51]. Figure 11 provides additional details on the RoB assessment results for the included RCTs.4. DiscussionTo evaluate the effects of collagen supplements on skin aging, we analyzed 26 RCTs to assess the efficacy of oral collagen supplements on skin hydration and elasticity, both of which characterize skin aging. The trials measured skin hydration and elasticity on various areas of the body including the cheek, forearm, and forehead. By analyzing these parameters, our findings revealed that oral collagen supplements improved skin hydration and elasticity. The beneficial effects were significant after 8 weeks or more of HC supplementation.4.1. HydrationThe key molecule involved in skin moisture is hyaluronic acid, a glycosaminoglycan with a unique capacity to retain water molecules [52]. The most striking histochemical change observed in aging skin is the gradual loss of epidermal hyaluronic acid [53]. Oral administration of collagen hydrolysates include rich proline-hydroxyproline, which stimulates hyaluronic acid production in the dermal fibroblast cells [54].Our study findings revealed that supplementation with oral collagens improved skin hydration, which is consistent with previous findings. Cao et al. reported that the concentration of moisture in the skin of mice treated with collagen peptides (CPs) was significantly higher compared with that of the control mice (p < 0.05) [55]. Sun et al. revealed that collagen as a single supplement showed remarkable effects on skin hydration, with an SMD of 0.77 (95% CI 0.60, 0.94; p < 0.00001) compared with a placebo [56].Our findings revealed that fish was the optimal source of collagen for improving skin hydration. A previous study indicated that collagens sourced from fish skins have diverse amino acid compositions than mammalian collagens [57]. Another study estimated that the yields of collagen derived from fish skin were 50%, collagen derived from fish bones were 40%, and collagen derived from fish fin were 36.4% [58]. Notably, marine collagen and collagen peptides have high bioavailability, potency, and a favorable safety profile [59].In our investigations, only one study by Schwartz (2019) investigated the effect of collagen sourced from chicken, which was the least among all included studies. However, in the study by Cao et al. on the effects of the oral intake of CPs derived from chicken bones in mice showed that the concentration of moisture in the skin of mice treated with CPs was significantly higher compared with that of the control mice (p < 0.05) [55]. Schwastz et al. administered 1 g of collagen from hydrolyzed chicken sternal cartilage daily for 12 weeks to all human participants. The skin hydration of the participants significantly increased by 12.5% (p = 0.003) between weeks 6 and 12 [36]. Additionally, it is unclear whether the results can be generalized to the wider population, as the studies were conducted on mice and humans with different characteristics and may not reflect the general population.4.2. ElasticityFibril-forming type I collagen is the major collagen in the skin, comprising 90% of the total collagen, and plays a role in structural organization, integrity, and strength and skin [60]. The elastic fiber network imparts elasticity and resilience to the tissues and comprises elastin and microfibrils, which are composed of various proteins [61]. The elasticity of the skin depends on the function of the network, and its formation is a complex process involving many factors. One study showed that the intake of HC downregulated placenta growth factor-2, insulin-like growth factor binding protein 2, insulin-like growth factor binding protein 3, platelet factor 4, serpin E1, and transforming growth factor β-1, and increased type I collagen mRNA and protein levels [62].Our findings revealed that supplementation with oral collagen improves skin elasticity, which are consistent with previous findings. De Luca et al. found that patients taking marine collagen peptides significantly improved skin elasticity (p < 0.0001) [63]. Maia Campos et al. demonstrated that a group treated with oral collagen showed significant differences in the mechanical properties of the skin compared with the baseline and placebo groups after 90 days of treatment only in the net elasticity parameter in the periorbital region [64]. Lee et al. showed that 12 weeks of oral collagen film consumption significantly increased the elasticity of the skin surface (R2), yielding 0.66 ± 0.05 before use to 0.75 ± 0.04 after 12 weeks (p < 0.05) [65]. The study conducted by Sone et al. (2018) was conducted on chronologically aged mice, which showed that oral administration of collagen peptides derived from bovine bone can improve the laxity of chronologically aged skin in mice by increasing the skin collagen content and ratio of type I to type III collagen. The study also suggested that collagen peptides may increase antioxidant properties in the body, and proline intake can improve the elasticity of chronologically aged skin in mice [66].Among the included studies, Yoon et al. showed that in humans, 12 weeks of supplementation with oral collagen significantly improved skin elasticity (3.25, 95% CI 2.33, 4.18) compared with other durations. This finding is consistent with that of an open, blinded, and noncomparative study, which showed 38.31% of improvement in elasticity after consuming oral collagen for 3 months [67]. Another study examined obvious characteristics of skin aging in nude mice after combining treatment with D-galactose and ultraviolet radiation. However, after the oral administration of CP, the concentrations of skin collagen and elastin increased [68]. While studies suggest that oral collagen supplementation may improve skin elasticity, it is important to consider the limitations of the research. The studies used different durations and forms of collagen supplementation, making it difficult to compare the results. Furthermore, the sample sizes of the studies were relatively small, and the human studies relied on self-reported measures of skin elasticity. Additionally, the study on nude mice may not accurately reflect the effects of oral collagen supplementation in humans.4.3. MechanismProtein hydrolysates are easier to digest and absorb than intact proteins, which increase the production of amino acids after meals [69]. An in vivo mouse model study found transient increases in the Gly-Pro-Hyp levels in the blood of both humans and mice and that other collagen peptides were also transported to the skin after the ingestion of HC [70]. Kamiyama et al. used [14C] Gly-Pro-Hyp as a tracer for the tripeptide and compared its absorption with 14C-labeled proline in rats. At 14 days after the administration of [14C] Gly-Pro-Hyp, almost all radioactivity disappeared from the organs, except for the skin, with a radioactivity of 70% observed after 6 h [71]. Another similar study observed radioactivity after a single administration of [14C] Gly-Pro-Hyp in the connective tissues including the bones and skin within 24 h [72].4.4. Sensitivity AnalysisIn this study, two included RCTs, namely Campos et al. [29] (2.17, 95% CI 1.52, 2.81) Choi et al. [32] (1.61, 95% CI 0.44, 2.78), yielded favorable effects of oral collagen supplementation on skin elasticity. Campos (2015) used a mixture of 10 g of collagen and vitamin A, C, E, zinc as well as excipients, which had beneficial effects, possibly because of its synergism with collagen. A study found that vitamin C triggers a considerable thickening of the epidermis, induces the production of collagen and the formation of elastic microfibrils [73]. By contrast, vitamin A maintains the health of the epithelial cells on the surface of the skin and increases the production of collagen and the extracellular matrix [74,75]. However, because Choi (2014) enrolled participants aged 30–48 years, which were younger than the participants in the other included studies, it is possible that this study yielded better results due to factors such as a potentially lower prevalence of underlying health conditions or greater overall health among the younger participants. This might thus explain why this study yielded better results. A clinical study that contributed that the composition of the basement membrane changed with age showed that the concentrations of collagen IV, collagen IV, and collagen XII decreased over time [76]. Thus, a sensitivity analysis was performed to assess the influence of these two studies, and the results of the corresponding forest plots are provided in the Supplementary Materials. The exclusion of this study resulted in no significant change, and the effects of collagen supplementation remained favorable.4.5. LimitationsThis study had several limitations. First, the interventions used in the included studies exhibited some heterogeneity, primarily because of the distinct measurement units and composition of the supplementation. Second, the number of patients included in some studies was less than 40. Therefore, a small sample size may have resulted in a slight RoB. Third, the patients’ lifestyle habits were not included in the analysis. For example, HC supplementation in patients with healthier lifestyle habits could have presented more evident results in improving the appearance of the skin. Thus, additional studies, specifically large clinical trials, are needed.5. ConclusionsThe findings of this study revealed that HC supplementation can improve skin hydration and elasticity. In addition, the long-term use of collagen yields more favorable effects on skin hydration and elasticity than the short-term use of collagen. Nevertheless, large-scale randomized control trials are required to examine the clinical benefits of oral collagen supplements.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.Supplementary MaterialsThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu15092080/s1, Figure S1. Elasticity-sensitivity analysis; Figure S2. Hydration-sensitivity analysis.Click here for additional data file.Author ContributionsConceptualization: S.-Y.P.; Data curation: S.-Y.P. and Y.-N.K.; Formal analysis: S.-Y.P. and C.C.; Funding acquisition: Y.-L.H.; Investigation: C.C.; Methodology: S.-Y.P., Y.-L.H., C.-M.P. and C.C.; Project administration: C.-M.P., Y.-N.K., K.-H.C. and C.C.; Software: C.-M.P., Y.-N.K. and C.C.; Supervision: C.C. and C.-M.P.; Validation: S.-Y.P., Y.-L.H., C.-M.P. and C.C.; Visualization: S.-Y.P.; Writing—original draft: S.-Y.P., C.-M.P. and Y.-L.H.; Writing—review & editing: K.D.H., K.-H.C. and C.C. All authors have read and agreed to the published version of the manuscript.Institutional Review Board StatementThis study did not require ethical approval.Informed Consent StatementThis study did not involve humans. Data Availability StatementData will be made available on reasonable request.Conflicts of InterestThe authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.References1.
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Figure 1Flowchart of the systematic review and meta-analysis according to the PRISMA guidelines.Figure 2Forest plot of the included studies evaluating skin hydration in patients supplemented with HC and patients in the placebo group [26,27,28,30,31,32,33,34,35,39,40,43,44,46,47,48,49,50]. (HC: hydrolyzed collagen, CI: confidence intervals, SD: standard deviation, I2: heterogeneity).Figure 3Forest plot of the included studies evaluating skin elasticity in patients supplemented with HC and patients in the placebo group [26,27,28,29,31,32,33,34,35,37,39,40,41,42,43,44,47,48,49]. (HC: hydrolyzed collagen, CI: confidence intervals, SD: standard deviation, I2: heterogeneity).Figure 4Forest plot for the subgroup analysis of skin hydration expressed as HC originating from fish, bovine, chicken, porcine, and unknown source in patients supplemented with HC and patients in the placebo group [26,27,28,30,31,32,33,34,35,40,43,44,46,47,48,49,50]. (HC: hydrolyzed collagen, CI: confidence intervals, SD: standard deviation, I2: heterogeneity).Figure 5Forest plot for the subgroup analysis of skin hydration expressed as 2, 4, 6, 8, and 12 weeks in patients supplemented with HC and patients in the placebo group [26,27,28,29,30,31,32,33,34,35,39,40,43,44,46,47,48,49,50]. (HC: hydrolyzed collagen, CI: confidence intervals, SD: standard deviation, I2: heterogeneity).Figure 6Forest plot for the subgroup analysis of skin hydration expressed as long-term (>8 weeks) and short-term (<8 weeks) in patients supplemented with HC and patients in the placebo group [26,27,28,30,31,32,33,34,35,39,40,43,44,46,47,48,49,50]. (HC: hydrolyzed collagen, CI: confidence intervals, SD: standard deviation, I2: heterogeneity).Figure 7Forest plot for the subgroup analysis of skin elasticity expressed as HC originating from fish, bovine, chicken, porcine, and unknown source in patients supplemented with HC and patients in the placebo group [26,27,28,29,31,32,33,34,35,37,39,40,41,42,43,44,47,48,49]. (HC: hydrolyzed collagen, CI: confidence intervals, SD: standard deviation, I2: heterogeneity).Figure 8Forest plot for the subgroup analysis of skin elasticity expressed as R2 (Gross elasticity), R5 (Net elasticity; elastic portion of relaxation/elastic portion of suction), R7 (Elastic portion; elastic portion of relaxation/first maximum amplitude after suction), and mm in patients supplemented with hydrolyzed collagen (HC) and patients in the placebo group [26,28,29,31,33,34,35,37,39,41,43,48,49]. (HC: hydrolyzed collagen, CI: confidence intervals, SD: standard deviation, I2: heterogeneity).Figure 9Forest plot for the subgroup analysis of skin elasticity expressed as 2, 4, 6, 8, and 12 weeks in patients supplemented with HC and patients in the placebo group [26,27,28,29,31,32,33,34,35,37,39,40,41,42,43,44,47,48,49]. (HC: hydrolyzed collagen, CI: confidence intervals, SD: standard deviation, I2: heterogeneity).Figure 10Forest plot for the subgroup analysis of skin elasticity expressed as long-term (>8 weeks) and short-term (<8 weeks) in patients supplemented with HC and patients in the placebo group [26,27,28,29,31,32,33,34,35,37,39,40,41,42,43,44,47,48,49]. (HC: hydrolyzed collagen, CI: confidence intervals, SD: standard deviation, I2: heterogeneity).Figure 11Risk of bias [18,26,27,28,29,30,31,32,33,34,35,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51]. * D1: Randomization process; D2: Deviations from the intended interventions; D3: Missing outcome data; D4: Measurement of outcome; D5: Selection of the reported result.

COMPLETE ASSIGNED SUPPLEMENT: nutrients-2311071 supplementary.pdf (one page).
1 
 
 
Figure S1. Elasticity-sensitivity analysis. [26–28, 31,33-35,37,39–44,47–49] 
 
 
Figure S2. Hydration-sensitivity analysis. [26–28,30–31,33-35,39,40,43,44,46–50] 

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379blackwellopenJournal of Cosmetic DermatologyJ Cosmet DermatolPMC13618891136188911361889110.1111/jocd.71208Hydrolyzed Collagen for Skin Aging Meta‐Analyses Are UnreliableGrimesDavid Robert1✉MahtoAnjali2
1
TCD Biostatistics Unit, Discipline of Public Health and Primary Care, School of Medicine, Trinity College Dublin, Dublin, Ireland
2
Self London, London, UK*

Correspondence:
 
David Robert Grimes (davidrobert.grimes@tcd.ie)
✉Corresponding author.28920262510e71208e712082992026© 2026 The Author(s). Journal of Cosmetic Dermatology published by Wiley Periodicals LLC.This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.ABSTRACTBackgroundScientific evidence for oral collagen supplementation in skin aging remains controversial and limited, with various trials finding conflicting results on skin parameters such as reduction in wrinkle depth, increases in hydration and elasticity. These parameters are measured with a variety of methodologies, and recent meta‐analyses have attempted to synthesize existent evidence to ascertain potential benefits. The question of whether these measures are readily comparable, however, has led to concerns about the internal validity and interpretability of these efforts.AimsTo investigate major existing meta‐analyses on oral collagen supplementation and perform a technical re‐analysis of both methodology and reported results, as well as a full meta‐regression of constituent collagen studies accounting for dose effects to ascertain evidence quality.MethodsData investigation and evidence appraisal of the methodology of existing meta‐analyses of collagen supplementation and implementation of meta‐regression accounting for cumulative dose effects.ResultsIn both identified meta‐analyses, we find inaccurate reporting of data from primary studies and inappropriate combinations of measures likely to give rise to spurious results. Meta‐regression of collagen studies accounting for dose variations in primary studies found no evidence of benefit from collagen supplementation.ConclusionIn sharp contrast to the reported results from prior meta‐analysis, this investigation does not find evidence of benefit for supplementation from existing collagen studies. Evidence synthesis for collagen studies needs a stronger methodological underpinning if results are to be clinically relevant.statusreleaseddisplay-pdfyesis-in-collection-domainyesis-olfnois-manuscriptnois-preprintnois-journal-matternois-scannednois-retractednoRevised 2026 Aug 28; Received 2026 Mar 23; Accepted 2026 Sep 8; Issue date 2026 Oct.

1.IntroductionCollagen is the principal structural protein in the dermis, comprising about 80% of skin's dry weight and providing tensile strength and elasticity [1]. Age‐related decline in collagen production and fibroblast activity leads to progressive dermal thinning, loss of elasticity, dehydration, and wrinkle formation [2]. These biological changes have driven interest in interventions that might restore collagen and ameliorate skin aging. In recent years, oral hydrolyzed collagen supplements have surged in popularity as a purported antiaging nutraceutical. A growing number of nutraceutical companies market collagen peptides in powders and drinks, claiming benefits such as wrinkle reduction and improved skin hydration. This trend has been amplified by aggressive industry marketing and endorsements on social media, fueling consumer enthusiasm [3]. As a result, dermatologists are increasingly faced with patients who are using or inquiring about collagen supplements for skin health, and clinicians must critically assess the evidence behind these products.Despite widespread commercial claims, the scientific evidence for oral collagen supplementation in skin aging remains controversial and limited. Small randomized controlled trials (RCTs) have been conducted, often sponsored by supplement manufacturers, to evaluate whether daily collagen peptide ingestion can improve skin appearance. Several of these trials report modest improvements in skin parameters such as reduction in wrinkle depth or increases in hydration and elasticity after 1–3 months of collagen use [4]. These findings have led to multiple systematic reviews and meta‐analyses concluding that hydrolyzed collagen supplementation produces statistically significant benefits for aging skin. Most recently, a 2021 meta‐analysis of 19 trials (1125 participants) reported that 90 days of oral collagen led to measurable reductions in wrinkles along with improved skin elasticity and hydration compared to placebo [4]. Similarly, a 2023 systematic review including 26 RCTs found significant pooled improvements in skin hydration and elasticity with collagen supplements [2]. At face value, such results suggest that collagen nutraceuticals may indeed counteract some aspects of skin aging.However, there are substantial methodological concerns that temper these optimistic conclusions. The current body of evidence is fraught with heterogeneity and potential bias. Trials of collagen for skin vary widely in design and quality: study populations range from young adults to the elderly; intervention regimens differ in collagen source (e.g., fish, porcine, bovine, or chicken origin), daily dosage (from as low as ~0.25 g to as high as 10–12 g of collagen peptides), and treatment duration (from 2 to 12 weeks). Outcomes are assessed using diverse measures, from objective instruments for hydration and elasticity to subjective wrinkle grading, making it difficult to standardize results across studies. This variability has led to considerable between‐study heterogeneity in meta‐analytic analyses. Furthermore, many trials have been small (often < 100 participants) and of short duration, which limits their power to detect meaningful long‐term effects [3].Perhaps most importantly, an overwhelming proportion of studies are industry‐funded or involve authors with commercial ties to collagen manufacturers, raising concerns about selective reporting and bias. Indeed, dermatologists and researchers have grown wary that the marketing‐driven popularity of collagen supplements may be outpacing the rigor of the underlying science. In light of these issues, a critical appraisal of the evidence is needed. This article examines the quality of existing clinical research on hydrolyzed collagen supplementation for skin aging, evaluates the reliability of reported benefits, and discusses whether current evidence is sufficient to support recommending these supplements in dermatologic practice.2.Methodological Issues With Existing Meta‐Analyses2.1.Concerning Levels of HeterogeneityIn the pooled analysis, de Miranda et al. [4] find ostensibly beneficial effects of collagen supplementation on skin hydration, skin elasticity, and skin roughness using standardized mean differences (SMDs) for all these measures respectively. However, there are extremely high indicators of heterogeneity, with I2 values of 93%, 98%, and 93% respectively. Even in the author's subgroup analyses, this remains exceptionally high, ranging from a minimum of 76% to a maximum of 99%. In Pu et al. [2], heterogeneity is 70% for the hydration group and 84% for skin elasticity measurements. These high levels of heterogeneity are indicative of significant variation between studies, and suggest potential differences in the populations and interventions which require deeper inspection.2.2.Inappropriate Use of SMDs
In both meta‐analyses, the authors pool incompatible measures in sometimes methodologically inappropriate ways. In Miranda et al., to quantify skin hydration effects, the authors initially perform a meta‐analysis of studies contrasting placebo and HC groups across nine studies. Of these, the authors identify five with the outcome measure micrometers, two with percentage change, and a further two with arbitrary units (AU) in patients. Similarly, the authors looked at studies of skin elasticity, using 14 studies which used measures including pressure (four studies), percentage change (six studies), and AUs (four studies) as outcome measures. Pu et al. includes many of the studies in Miranda et al. and performs a similar exercise in grouping differing outcomes. For all these studies, the authors calculated SMDs to combine and analyze results together. This implicitly assumes that SMD treats everything on a standardized scale and that differences in units can be “standardized away.”This is wrong; however, as SMD only works as a basis for comparison when the underlying constructs being measured are the same, with variation in scale the main difference. This is not valid when the meaning or structure of the outcome differs; percentage change, for example, is bounded and behaves differently from unbounded AUs. Nor is percentage change in elasticity equivalent to raw change in Young's modulus. The use of SMD for comparative purposes requires similar variance across studies, but in devices with different metrics or AUs can have wildly different variability, rendering SMD unsuitable for cross‐comparison in these instances [5, 6]. This ultimately renders clinical interpretation impossible, as there is no clear way to determine what a pooled SMD across incompatible measurement systems even means and masks critical differences in outcome structure, variance, and clinical meaning.2.3.Data and Analysis ErrorsIn Miranda et al., the authors of this work attempt to control for different metrics by performing subgroup analyses stratified by measurement, but these are plagued by data and analysis errors that confound conclusions, some of these errors we elucidate in the sections below. Similarly Pu et al. have several of the same errors that undermine the entire analysis. An overview of some of these is given in Table 1.TABLE 1Overview of data and analysis errors in de Miranda et al. and Pu et al.Skin hydration data reporting errors in de Miranda et al.Misrepresentation of methodologySubgroup analysis asserts five studies expressed skin hydration in micrometers. This is incorrect—none of the nine studies quoted do this. Instead, four use capacitance methods to measure relative skin hydration (measured in arbitrary units, AU) and one (Žmitek et al. [7]) uses conductance methods measured in microsiemens. In total, eight of the nine hydration studies used capacitance methods to measure skin hydrationIncorrect usage of percentage differenceBoth studies the authors state as measuring percentage difference (Koizumi et al. [8], Schwartz et al. [9]) actually use conductance methods with the authors failing to convert reported percentage difference to AUFailure to convert standard error to standard deviationThe meta‐analysis authors also treated standard error from one study as the standard deviation (Koizumi et al. [8]) which should have been converted prior to analysis to avoid false positive resultsMissing dataA study including hydration analysis (Yoon et al. [10], nonsignificant finding) was used for skin elasticity, but missed in meta‐analysis on hydrationSkin elasticity data reporting errors in de Miranda et al.Inclusion of studies with no placebo controlOf the 14 included studies pertaining to skin elasticity, Proksch et al. [11] did not compare HC to placebo and should not have been included in analysisFailure to distinguish standard error from standard deviationThe authors incorrectly treated standard error from one study as standard deviation (Genovase et al. [12])Incorrect usage of percentage differenceOf the six studies the authors claim use percentage differences and four the authors claim used arbitrary units in subgroup analysis, all 10 actually used cutometer methods (8× R2, 1× R5, 1× R7). In total, 10 of the 13 eligible studies used suction‐based cutometer measurements and three pressure measurementsSkin hydration data reporting errors in Pu et al.Missing studiesOf the 18 studies the authors asserted to have included the hydration analysis, one of these cited in the meta‐analysis table was not included in the paper references (Lin et al. [13]) and was explicitly referred to as excluded in the paper's textInappropriate inclusionOne paper's hydration data was based on a survey question without any objective analysis (Evans et al. [14]) and should not have been includedErrors in effect size reportingConfusion of percentage differences as effect sizes, most obviously with Yoon et al. [10], reported by Pu et al. report as highly significant in their meta‐analysis despite the original authors explicitly stating the result was not significant. Percentage conversion not considered for other studies (Jung et al. [15] and Miyanaga et al. [16])Failure to convert standard error to standard deviationThe authors also mistakenly cited two papers reporting standard error the mean as standard deviation (di Cerbo et al. [17] and Koizuma et al. [8]), producing false positive resultsSkin elasticity data reporting errors in Pu et al.Incorrect usage of percentage differenceFailure to convert percentage difference to measurement differences in some included papers (Koizumi et al. [8] and Yoon et al. [10])Failure to distinguish standard error from standard deviationGenovase et al. [12] was included without converting SEM to SD, skewing analysisIncorrect numbers of subjectsFor one study (Kim et al. [18]), the total initially enrolled subjects is reported in the table rather than the total analyzed subjects. In total, 14 of the 18 eligible studies used suction‐based cutometer measurements, three used pressure measurements (same as de Miranda et al.) and one used unclear modality (di Cerbo et al. [17])2.4.Analysis Issues and Lack of Dose CorrectionAside from the fact that correcting the data entry issues markedly changes conclusions of the meta‐analyses, a more relevant issue was not considered appropriately in either analysis—that of HC dose effects. In de Miranda et al., daily collagen dose varied from 0.3 to 10 g/day, an over 33× fold difference. In Pu et al., it values from 0.248 g a day to 10 g, an over 40× variation. A standard meta‐analysis that ignores doses effectively treats all doses as equivalent, implicitly assuming that treatment effect is binary. This is highly unlikely to be true and confounds interpretation and introduces study heterogeneity. For this reason, the analysis presented in both de Miranda et al. and Pu et al. is likely misleading.2.5.Potential Risk of Bias IssuesIn de Miranda et al., the authors did not indicate whether studies were industry supported or conducted in their risk of bias ranking. In that work, of the nine hydration reporting studies, seven were industry sponsored. Neither of the two nonindustry sponsored studies found significant effects of HC on skin hydration, whereas four of the seven industry sponsored studies did. Of the 13 eligible studies reporting skin elasticity in that meta‐analysis, 10 were industry sponsored or conducted. None of the three nonindustry sponsored studies found significant effects of HC on skin elasticity, whereas four of the 10 industry sponsored studies did.Similar issues were seen in Pu et al., where of the 16 hydration studies with objective evidence, 13 were industry‐conducted or sponsored. None of the three nonindustry funded studies found any significant effect, whereas six of the 13 industry studies did. For elasticity, 12 of the 17 studies were industry conducted or funded. Of these, five of six studies yielding a positive effect of HC were industry funded, whereas only two of the four studies finding a negative impact of HC were industry funded. In the remaining seven nonsignificant studies, five of which were industry funded.3.Meta‐Regression With Corrected DataFor the reasons outlined in the prior section, a more rigorous approach to ascertain potential impact of collagen supplementation would be to perform a meta‐regression with either daily dose or cumulative dose over the study period as a continuous predictor.To investigate whether the conclusion of these meta‐analyses would hold if considering dose, we took corrected data (with corrections from the issues listed in Table 1) from both meta‐analysis and the dose data reported in the original papers, performing a simple meta‐regression analysis for both daily dose and cumulative dose (the product of daily dose over the entire duration of the study). The number of weeks on treatment was also considered as a covariate.For both hydration and skin elasticity in both meta‐analyses, we also performed the meta‐regression on all included studies from both meta‐analyses with SMD as a measure, once units were corrected. We further performed subgroup meta‐regression on studies grouped by comparable methodologies, using mean differences as a basis for comparison.4.ResultsAs shown in Table 2, no analysis showed any significant differences between placebo and HC groups with dose from either meta‐analysis. Full data and relevant code are given in the Supporting Information. Additionally, we synthesized all unique results from both meta‐analyses to provide an updated meta‐regression of dose effects (16 unique studies on hydration, 18 on elasticity). These results, as shown in Figure 1 do not support the interpretation that HC supplementation is beneficial for either skin hydration or elasticity.TABLE 2Meta‐regression of HC/placebo differences with daily dose for both meta‐analyses.AnalysesMeta‐regressionsIncluded studiesDose coefficient
p‐value

I2
de Miranda et al. [4]Hydration studiesAll eligible studies90.03 (−0.10, 0.15)0.6674.5%Capacitance methods80.03 (−0.10, 0.15)0.6977.3%Elasticity studiesAll eligible studies13−0.10 (−0.67, 0.46)0.7299.0%Pressure methods3−4.89 (−21.53, 11.79)0.5799.5%Suction methods
a

10−0.09 (−0.30, 0.12)0.4192.6%Pu et al. [2]Hydration studiesAll eligible studies160.07 (−0.01, 0.14)0.0858.8%Capacitance methods140.07 (−0.01, 0.15)0.0863.4%Elasticity studiesAll eligible studies180.02 (−0.35, 0.39)0.9298.6%Pressure methods3−4.89 (−21.53,11.79)0.5799.5%Suction methods
a

140.04 (−0.14,0.22)0.7094.1%
a
Suction methods included R2, R5, and R7 measurements.FIGURE 1Meta‐regressions of all eligible studies in both meta‐analyses with dose as a covariate for (a) hydration studies (p = 0.08, I2=58.8%) and (b) elasticity studies (p = 0.92, I2=98.6%). Shaded region lines depict 95% confidence intervals, with circle size inversely proportional to study reported variability, where smaller circles indicate less variance. Vertical axis is standardized mean difference. Note that cumulative dose is shown here for illustration, but no significant relationships were found using daily dose or time on treatment in weeks as predictors.5.DiscussionDespite the growing popularity of hydrolyzed collagen supplements as an intervention for skin aging, the current clinical evidence base remains methodologically fragile, inconsistent, and prone to bias. While several meta‐analyses have reported statistically significant improvements in hydration, elasticity, and skin roughness following oral collagen supplementation, the critical re‐evaluation outlined in this work reveals that many of these findings rest on flawed statistical assumptions, inadequate trial designs, and uncorrected sources of bias. A further limitation was that included studies in published meta‐analyses also include trials where collagen was not used in isolation, but as part of a multicomponent formulation with other bioactive ingredients. This limits not only the generalizability of results from these meta‐analyses, but applies to the synthesis in this work. It further underscores the reality that collagen meta‐analyses have underlying data problems in addition to the issues we discuss in this work. The inclusion of other potentially active agents alongside collagen may themselves affect aspects like skin hydration, elasticity, or appearance either positively or negatively. Previous meta‐analyses and investigations have made little effort to account for this methodological limitation, rendering it even more difficult to attribute observed effects specifically to hydrolyzed collagen. This is a methodological limitation future analyses should strive to avoid.One of the most striking issues is the extreme heterogeneity across included studies. Previously published pooled analyses report I
2 values exceeding 90% for hydration, elasticity, and roughness outcomes [2]. In a closer re‐examination, this heterogeneity remained high even within subgroup analyses, raising concerns regarding the validity of the aggregated effect estimates. Much of this inconsistency arises from inappropriate pooling of fundamentally incompatible outcome measures using SMDs. In one widely cited meta‐analysis, skin hydration outcomes were combined from studies reporting in AUs, percentage change, and conductance (microsiemens), with no consideration given to the bounded nature or differential variance structures of these measures. Similarly, elasticity outcomes were pooled despite being measured via differing constructs: Young's modulus in megapascals (MPa), cutometry‐derived indices, and percentage changes in recoil time or deformation resistance. These metrics do not share the same underlying physiological interpretation and therefore cannot be considered interchangeable. This is not merely a difference in units of hydration or elasticity; it is ultimately an attempt to pool measures so physiologically distinct that the pooling of them represents a serious conceptual limitation. SMDs assume comparability of construct and distribution; this is invalid when the nature, clinical meaning, and scale of measurement differ so profoundly [19].Subgroup analysis by measurement method demonstrates this clearly. In hydration studies, a reclassification of outcome metrics showed that eight of nine trials used capacitance‐derived AUs, and only one used conductance. Contrary to the original subgrouping, which incorrectly stated that five studies measured hydration in micrometers, none used this metric. A similar issue was identified in the elasticity domain. Of the 14 studies included in a prior meta‐analysis, one did not compare collagen to placebo and should have been excluded entirely, while another was mis‐analyzed due to incorrect variance calculations (using standard error instead of standard deviation). Additionally, the authors' categorization of elasticity measures into pressure‐based versus AU subgroups failed to acknowledge that 10 of the 13 valid studies used suction‐based cutometer measurements, specifically R2 (gross elasticity), R5 (net elasticity), or R7 (biological elasticity). These parameters are physiologically distinct: R2 represents total elastic recovery; R5 reflects the net elastic portion independent of viscous response; and R7 approximates the skin's ability to return to its original state after deformation. Pooling these without stratification obscures potential signal variance and introduces interpretative ambiguity. Notably, none of the included studies found consistent improvements across all three parameters; instead, findings were sporadic and method‐specific. The issue of dose variability also undermines pooled effect estimates. Across hydration and elasticity studies, collagen doses ranged from 0.3 to 10 g/day, i.e., an over 30‐fold difference.The meta‐regression analyses in this work differed from previous undertakings by using dose as a continuous predictor (both daily and cumulative dose) which previous meta‐analysis had not considered despite its confounding influence. Meta‐regression showed no significant relationship between dosage and clinical outcome, either in mean difference models or SMD‐based comparisons. Most strikingly, there is an absence of a dose–response relationship that questions the plausibility of a genuine pharmacodynamic effect. It suggests that observed differences may be attributable to bias, noise, or behavioral confounders rather than any true biological gradient. If collagen's effects were mechanistically driven, one would anticipate a correlation between dose intensity and magnitude of change, yet this was not observed. This is a distinct strength of meta‐regression over meta‐analysis, and as the treatment time varied in the constituent trials, we regressed with respect to both dose per treatment and cumulatively over the course of treatment, finding no dose effects under either model. This work is the first to our knowledge to control for dose in regression, and that it fails to find any dose dependent effect suggests that ostensible benefits reported in previous meta‐analysis were likely spurious findings based on using a methodology that did not account for dose.Bias related to industry sponsorship further clouds interpretation. Among hydration studies, seven of nine were funded by commercial manufacturers. Of these, four reported statistically significant results, whereas neither of the two independent studies did. Similar patterns were noted in elasticity trials, where 10 of 13 studies were industry funded; all statistically significant findings arose from these, while the three nonsponsored studies reported no benefit. These trends echo those identified in recent meta‐analyses by Myung and Park [20], who showed that collagen's apparent efficacy vanished when limited to independently funded or high‐quality trials. Such findings underscore the persistent risk of sponsorship bias and selective reporting, well‐documented phenomena in pharmaceutical and nutraceutical research [21].Beyond statistical and funding‐related concerns, broader methodological limitations undermine the field. Many studies are of short duration, typically 8–12 weeks, and therefore may be poorly suited to assess outcomes related to intrinsic or extrinsic skin aging, which may progress over years. If oral collagen supplementation has a clear biological effect, then it may involve mechanistic processes like extracellular matrix remodeling, collagen turnover, or other structural changes in dermal architecture which occur gradually and might be missed over too short a clinical frame. There may be effects a clinician expects to appear by 8 weeks. Equally, there also might be effects that do not manifest by 8 weeks but may be apparent by 12 weeks. In other cases, a longer period might be required to see effects. Thus, a mechanistic justification of when an effect would be expected based on clinical expertise or preexisting data is critical in future work. Without this experimental justification, the question of whether such short duration studies are clinically valuable or not remains, but this is a question beyond the scope of this manuscript. While some studies report modest improvements in hydration or elasticity, the clinical significance of these changes is uncertain. For example, a 5% increase in skin hydration measured via corneometry may not be perceptible to the patient nor translate into a visible reduction in fine lines or textural change. Most trials also fail to report whether outcomes crossed any predefined minimal clinically important difference (MCID), and none included long‐term follow‐up to determine whether transient improvements were sustained beyond supplementation cessation.Another overlooked but critical factor is regression to the mean. Subjects enrolled into trials may often do so during periods of perceived cosmetic decline, such as seasonal dryness or fatigue‐related skin change, and may naturally improve over time regardless of intervention. This is particularly relevant in small trials with subjective outcomes, where participants and assessors are aware of the trial's goals, and blinding may be suboptimal. Indeed, many of the included studies provided limited detail on placebo formulation. Given that collagen peptides have a distinctive taste, mouthfeel, and nutritional profile, inadequate placebo matching may result in partial or full unblinding. This, combined with participants' expectation of benefit, introduces risk of performance and detection bias, especially for subjective endpoints such as “glow,” “radiance,” or self‐reported improvement in wrinkle severity.Expectancy effects may also be amplified by the socio‐demographic characteristics of typical study participants. Those with the means and motivation to invest in oral collagen are often simultaneously engaging in other proskin behaviors: daily sunscreen use, prescription, or over‐the‐counter retinoids, antioxidant serums, professional treatments such as microneedling or chemical peels, or energy‐based devices. Few trials controlled for these variables or monitored skincare regimens during the study period. It is therefore plausible that any observed improvement reflects a clustering of health‐seeking behaviors rather than a specific effect of collagen. An additional consideration is the potential contribution of improved nutritional status. Oral collagen is a form of hydrolyzed protein, and supplementation may inadvertently increase overall amino acid availability, particularly in individuals with previously suboptimal dietary intake. Few studies reported baseline protein consumption, and none stratified participants by dietary adequacy. In such cases, modest improvements in skin quality could plausibly reflect enhanced systemic nutrition rather than a targeted dermal mechanism of action. This remains a poorly controlled source of confounding.These concerns are compounded by analytic flaws in the broader evidence synthesis. Many meta‐analyses conducted multiple unadjusted subgroup analyses (by dose, collagen source, duration, or age), yet none corrected for multiplicity or adjusted confidence intervals accordingly. When sufficient subgroup comparisons are made, the probability of Type I error increases substantially. Without correction, some statistically significant findings are almost certainly false positives. Additionally, none of the existing meta‐analyses incorporated trial sequential analysis (TSA) to assess whether the accumulated sample size was adequate to support the claimed conclusions. Given the modest number of participants across all trials (approximately 1700 collectively), and the frequent borderline p‐values, many positive findings are likely to be artefactual. A lack of TSA and multiplicity correction undermines confidence in any “significant” effects emerging from this evidence base [22, 23]. As it stands, many of these analyses and their constituent trials may be considered research waste [24, 25], too poorly conducted to answer their own fundamental research question.Beyond the literature, the widespread uptake of collagen supplements in clinical and consumer settings reflects factors that extend beyond scientific merit. Collagen's popularity has been driven in part by its intuitive biological narrative, namely, that age‐related collagen loss can be reversed by oral supplementation. This appealing, though mechanistically simplistic, concept has been amplified through direct‐to‐consumer marketing, esthetic conference sponsorship, peer‐to‐peer endorsements, and social media influencer advocacy. Unlike pharmaceuticals, nutraceuticals such as hydrolyzed collagen are not required to demonstrate efficacy in large‐scale trials prior to market release. Early, industry‐sponsored studies reporting modest cosmetic improvements, often in underpowered or methodologically limited designs, were sufficient to support product claims in the absence of regulatory scrutiny. As a result, many clinicians and patients have adopted collagen as a low‐risk intervention, despite a lack of high‐quality, independent evidence. This diffusion of practice underscores the need for rigorous, critical appraisal within dermatology and a cautious approach to integrating commercially promoted supplements into routine care.Until such evidence emerges, oral collagen supplementation should not be promoted or recommended within dermatological practice. National societies such as the British Association of Dermatologists (BAD), European Academy of Dermatology and Venereology (EADV), and relevant regulatory bodies should consider issuing guidance advising clinicians against endorsing collagen supplements for skin aging in the absence of high‐quality, independent evidence. Collagen may yet have a role in dermatology, but at present, its promotion as a validated antiaging intervention is premature and not scientifically defensible.6.ConclusionsCurrent evidence does not support the recommendation of oral hydrolyzed collagen as a treatment for skin aging. When studies are grouped by compatible measurement, when analytical and methodological errors are corrected, and when dose, sponsorship, and bias are accounted for, the purported benefits of collagen supplementation do not persist. Any marginal improvements observed appear highly contingent on outcome selection, short‐term fluctuations, and industry affiliation. They are not robust across endpoints, not dose‐responsive, not seen in independent trials, and not demonstrably clinically meaningful. Given the strength of public interest and widespread clinical uptake, further research is warranted. However, future trials must be free from commercial bias, adequately powered, and transparently reported. Studies should prespecify primary outcomes with appropriate MCIDs, control for skincare confounders, match placebos rigorously, and include long‐term follow‐up. Trials should also report full protocols and raw data to allow independent re‐analysis.DisclosurePatient and Public Involvement: Both authors are highly involved in medico‐scientific public outreach and this work stemmed from multiple requests by public and patient partners to ascertain evidence quality for collagen supplementation. It was not appropriate or possible to involve patients or the public in the design, or conduct of the research directly, but results of this analysis will be made available to public for dissemination and discussion.Ethics StatementThe authors have nothing to report.ConsentThe authors have nothing to report.Conflicts of InterestThe authors declare no conflicts of interest.Supporting information
Data S1: jocd71208‐sup‐0001‐Supinfo.rar.Data Availability StatementThe basic R code and data sets used for analysis are available with this publication.References1.
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de Boer M. R., Waterlander W. E., Kuijper L. D. J., Steenhuis I. H. M., and Twisk J. W. R., “Testing for Baseline Differences in Randomized Controlled Trials: An Unhealthy Research Behaviour That Is Hard to Eradicate,” International Journal of Behavioral Nutrition and Physical Activity
12 (2015): 4, 10.1186/s12966-015-0162-z.20.
Myung S. K. and Park Y., “Effects of Collagen Supplements on Skin Ageing: A Systematic Review and Meta‐Analysis of Randomised Controlled Trials,” American Journal of Medicine
138, no. 9 (2025): 1264–1277.21.
Lundh A., Sismondo S., Lexchin J., Busuioc O. A., and Bero L., “Industry Sponsorship and Research Outcome,” Cochrane Database of Systematic Reviews
12 (2012): MR000033, 10.1002/14651858.MR000033.pub2.22.
Wetterslev J., Jakobsen J. C., and Gluud C., “Trial Sequential Analysis in Systematic Reviews With Meta‐Analysis,” BMC Medical Research Methodology
17 (2017): 39, 10.1186/s12874-017-0315-7.23.
Turner R. M., Bird S. M., and Higgins J. P. T., “The Impact of Study Size on Meta‐Analyses: Examination of Underpowered Studies in Cochrane Reviews,” PLoS One
8, no. 3 (2013): e59202, 10.1371/journal.pone.0059202.24.
Grimes D. R., Is Biomedical Research Self‐Correcting? Modelling Insights on the Persistence of Spurious Science, vol. 11 (Royal Society Open Science, 2024).25.
Glasziou P. and Chalmers I., “Research Waste Is Still a Scandal—An Essay by Paul Glasziou and Iain Chalmers,” BMJ
636 (2018): k4645.Associated DataSupplementary Materials
Data S1: jocd71208‐sup‐0001‐Supinfo.rar.Data Availability StatementThe basic R code and data sets used for analysis are available with this publication.

COMPLETE SUPPLEMENT MEMBER: ReadMe.txt
README.TXT
------------
The spreadsheets in this work contain the extracted data from collagen hydration and elasticity studies contained within the meta-analyses examined. Percentage conversions were performed using a delta-method, papers quoting SEM were converted to SD for consistency and cross-comparison.

The associated R files perform meta-regression on this data, and produce relevant figures. They are provided as is, and reproduce the figures given in the paper. For further information, please contact:

davidrobert.grimes@tcd.ie 


COMPLETE SUPPLEMENT MEMBER: elastic_regs_complete.R
library(metafor)
library(readxl)

setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
datain <- read_excel("allelasticfinal.xlsx")


# data in
dat <- data.frame(
  study = datain$study,
  n1 = datain$NE,
  mean1 = datain$ME,
  sd1 = datain$SE,
  n2 = datain$NC,
  mean2 = datain$MC,
  sd2 = datain$SC,
  dose = datain$dose,       # grams/day
  weeks = datain$Time,
  pressure = datain$Pressure,
  suction = datain$Suction,
  ma1 = datain$ma1,
  ma2 = datain$ma2
)
dat$total_dose <- dat$dose*dat$weeks*7


#Set to 1 for just suction methods, 2 for just pressure
methods <- 0
if(methods ==1){
  dat <- dat[dat$suction==1,]
}

if(methods ==2){
  dat <- dat[dat$pressure==1,]
}

#if you just want De Miranda, set this to 1
mir <- 0
if(mir==1){
  dat <- dat[dat$ma1 ==1,]
}

# Compute raw mean differences and variances
escalc_data <- escalc(
  measure = "SMD",
  m1i = mean1, sd1i = sd1, n1i = n1,
  m2i = mean2, sd2i = sd2, n2i = n2,
  data = dat
)

# Fit model with total dose and duration (weeks)
res_weekly <- rma(yi, vi, mods = ~ total_dose + weeks , data = escalc_data)


#Daily variant 
res_daily <- rma(yi, vi, mods = ~ dose + weeks , data = escalc_data)

#Dose variant 
res_dose <- rma(yi, vi, mods = ~ dose , data = escalc_data)
# Predict values across a sequence of daily doses (ignoring weeks)
new_dose <- seq(min(dat$dose), max(dat$dose), length.out = 100)
preds <- predict(res_dose, newmods = new_dose)  # res3 uses mods = ~ dose + weeks

bubble_sizes <- sqrt(1 / escalc_data$vi)
bubble_sizes <- pmax(bubble_sizes, 1.5)  # Set minimum bubble size to 1.5

# Plot raw effect sizes vs dose
plot(dat$dose, escalc_data$yi,
     xlab = "Daily Dose (g/day)",
     ylab = "Mean Difference",
     pch = 21, bg = "lightblue",
     cex =  bubble_sizes,  # Bubble size ~ precision
     main = "Elastic meta-Regression"
)

# Add regression line
lines(new_dose, preds$pred, col = "red", lwd = 2)

# Add confidence interval bands
lines(new_dose, preds$ci.lb, col = "red", lty = 2)
lines(new_dose, preds$ci.ub, col = "red", lty = 2)



library(metafor)

# Fit meta-regression with cumulative dose
res_cum <- rma(yi, vi, mods = ~ total_dose, data = escalc_data)

# Predict across a sequence of cumulative doses
new_total <- seq(min(dat$total_dose), max(dat$total_dose), length.out = 100)
preds_cum <- predict(res_cum, newmods = new_total)

# Bubble sizes ~ precision
bubble_sizes <- sqrt(1 / escalc_data$vi)
bubble_sizes <- pmin(bubble_sizes, 5)  # Limit max size

# Plot
plot(dat$total_dose, escalc_data$yi,
     xlab = "Cumulative Dose (g)",
     ylab = "Standardized Mean Difference (SMD)",
     pch = 21, bg = "lightblue",
     cex = bubble_sizes,
     main = "(b) Elasticity Meta-Regression vs Cumulative Dose"
)

# Add shaded confidence band
polygon(c(new_total, rev(new_total)),
        c(preds_cum$ci.lb, rev(preds_cum$ci.ub)),
        col = rgb(1, 0, 0, 0.2), border = NA)

# Add regression line
lines(new_total, preds_cum$pred, col = "red", lwd = 2)



COMPLETE SUPPLEMENT MEMBER: hyd_regs_complete.R
library(metafor)
library(readxl)

setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
datain <- read_excel("hydrodata.xlsx")


# 
dat <- data.frame(
  study = datain$study,
  n1 = datain$NE,
  mean1 = datain$ME,
  sd1 = datain$SE,
  n2 = datain$NC,
  mean2 = datain$MC,
  sd2 = datain$SC,
  dose = datain$dose,       # grams/day
  weeks = datain$Time,
  ma1 = datain$ma1,
  ma2 = datain$ma2,
  cap = datain$cap_method
)


dat$total_dose <- dat$dose*dat$weeks*7

#Change this to 1 to exclude non-capacitance methods
justcap <- 0
if(justcap ==1){
dat <- dat[dat$cap==1,]  
}


#De Miranda Meta Regression 
dat_mir <- dat[dat$ma1==1,]


# Compute raw mean differences and variances
escalc_data_mir <- escalc(
  measure = "SMD",
  m1i = mean1, sd1i = sd1, n1i = n1,
  m2i = mean2, sd2i = sd2, n2i = n2,
  data = dat_mir
)

# Fit model with total dose and duration (weeks)
res_weekly_mir <- rma(yi, vi, mods = ~ total_dose + weeks , data = escalc_data_mir)


#Daily variant 
res_daily_mir <- rma(yi, vi, mods = ~ dose + weeks , data = escalc_data_mir)

#Dose variant 
res_dose_mir <- rma(yi, vi, mods = ~ dose , data = escalc_data_mir)
# Predict values across a sequence of daily doses (ignoring weeks)
new_dose <- seq(min(dat_mir$dose), max(dat_mir$dose), length.out = 100)
preds <- predict(res_dose_mir, newmods = new_dose)  # res3 uses mods = ~ dose + weeks

bubble_sizes <- sqrt(1 / escalc_data_mir$vi)
bubble_sizes <- pmax(bubble_sizes, 1.5)  # Set minimum bubble size to 1.5

# Plot raw effect sizes vs dose
plot(dat_mir$dose, escalc_data_mir$yi,
     xlab = "Daily Dose (g/day)",
     ylab = "Mean Difference",
     pch = 21, bg = "lightblue",
     cex =  bubble_sizes,  # Bubble size ~ precision
     main = "(a) De Miranda Hydration meta-Regression"
)

# Add regression line
lines(new_dose, preds$pred, col = "red", lwd = 2)

# Add confidence interval bands
lines(new_dose, preds$ci.lb, col = "red", lty = 2)
lines(new_dose, preds$ci.ub, col = "red", lty = 2)



#Full regression (Pu includes all, same)

# Compute raw mean differences and variances
escalc_data <- escalc(
  measure = "SMD",
  m1i = mean1, sd1i = sd1, n1i = n1,
  m2i = mean2, sd2i = sd2, n2i = n2,
  data = dat
)

# Fit model with total dose and duration (weeks)
res_weekly <- rma(yi, vi, mods = ~ total_dose + weeks , data = escalc_data)


#Daily variant 
res_daily <- rma(yi, vi, mods = ~ dose + weeks , data = escalc_data)

#Dose variant 
res_dose <- rma(yi, vi, mods = ~ dose , data = escalc_data)
# Predict values across a sequence of daily doses (ignoring weeks)
new_dose <- seq(min(dat$dose), max(dat$dose), length.out = 100)
preds <- predict(res_dose, newmods = new_dose)  # res3 uses mods = ~ dose + weeks

bubble_sizes <- sqrt(1 / escalc_data$vi)
bubble_sizes <- pmax(bubble_sizes, 1.5)  # Set minimum bubble size to 1.5

# Plot raw effect sizes vs dose
plot(dat$dose, escalc_data$yi,
     xlab = "Daily Dose (g/day)",
     ylab = "Mean Difference",
     pch = 21, bg = "lightblue",
     cex =  bubble_sizes,  # Bubble size ~ precision
     main = "(b) Full Hydration meta-Regression"
)

# Add regression line
lines(new_dose, preds$pred, col = "red", lwd = 2)

# Add confidence interval bands
lines(new_dose, preds$ci.lb, col = "red", lty = 2)
lines(new_dose, preds$ci.ub, col = "red", lty = 2)



library(metafor)

# Fit meta-regression with cumulative dose
res_cum <- rma(yi, vi, mods = ~ total_dose, data = escalc_data)

# Predict across a sequence of cumulative doses
new_total <- seq(min(dat$total_dose), max(dat$total_dose), length.out = 100)
preds_cum <- predict(res_cum, newmods = new_total)

# Bubble sizes ~ precision
bubble_sizes <- sqrt(1 / escalc_data$vi)
bubble_sizes <- pmin(bubble_sizes, 5)  # Limit max size

# Plot
plot(dat$total_dose, escalc_data$yi,
     xlab = "Cumulative Dose (g)",
     ylab = "Standardized Mean Difference (SMD)",
     pch = 21, bg = "lightblue",
     cex = bubble_sizes,
     main = "(a) Hydration Meta-Regression vs Cumulative Dose"
)

# Add shaded confidence band
polygon(c(new_total, rev(new_total)),
        c(preds_cum$ci.lb, rev(preds_cum$ci.ub)),
        col = rgb(1, 0, 0, 0.2), border = NA)

# Add regression line
lines(new_total, preds_cum$pred, col = "red", lwd = 2)


COMPLETE SUPPLEMENT MEMBER: all-spreadsheet-cells-readonly.json
{
  "allelasticfinal.xlsx": {
    "xl/worksheets/sheet1.xml": [
      [
        [
          "A1",
          "study"
        ],
        [
          "B1",
          "method"
        ],
        [
          "C1",
          "dose"
        ],
        [
          "D1",
          "unit"
        ],
        [
          "E1",
          "Time"
        ],
        [
          "F1",
          "NE"
        ],
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          "ME"
        ],
        [
          "H1",
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        ],
        [
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          "NC"
        ],
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        ],
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        ],
        [
          "M1",
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        ],
        [
          "N1",
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        ],
        [
          "O1",
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        ],
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          "P1",
          "ma1"
        ],
        [
          "Q1",
          "ma2"
        ]
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        ],
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        ],
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        ],
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        ],
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        ],
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        ]
      ],
      [
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        ],
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        ],
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        ],
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        ],
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        ],
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        [
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        ]
      ],
      [
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          "miyanaga 2021"
        ],
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          "B12",
          "Cutometer model"
        ],
        [
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        [
          "E12",
          "12"
        ],
        [
          "F12",
          "33"
        ],
        [
          "G12",
          "0.84"
        ],
        [
          "H12",
          "4.3999999999999997E-2"
        ],
        [
          "I12",
          "33"
        ],
        [
          "J12",
          "0.81599999999999995"
        ],
        [
          "K12",
          "4.3999999999999997E-2"
        ],
        [
          "L12",
          "R2"
        ],
        [
          "M12",
          "industry"
        ],
        [
          "N12",
          "1"
        ],
        [
          "O12",
          "0"
        ],
        [
          "P12",
          "0"
        ],
        [
          "Q12",
          "1"
        ]
      ],
      [
        [
          "A13",
          "Nometo 2020"
        ],
        [
          "B13",
          "Cutometer CT580+MPA580;"
        ],
        [
          "C13",
          "10"
        ],
        [
          "D13",
          "Unitless"
        ],
        [
          "E13",
          "8"
        ],
        [
          "F13",
          "20"
        ],
        [
          "G13",
          "0.66"
        ],
        [
          "H13",
          "7.0000000000000007E-2"
        ],
        [
          "I13",
          "19"
        ],
        [
          "J13",
          "0.74"
        ],
        [
          "K13",
          "0.06"
        ],
        [
          "L13",
          "R2"
        ],
        [
          "M13",
          "industry"
        ],
        [
          "N13",
          "1"
        ],
        [
          "O13",
          "0"
        ],
        [
          "P13",
          "1"
        ],
        [
          "Q13",
          "1"
        ]
      ],
      [
        [
          "A14",
          "sangsuwan 2020"
        ],
        [
          "B14",
          "Cutometer MPA 580"
        ],
        [
          "C14",
          "5"
        ],
        [
          "D14",
          "Unitless"
        ],
        [
          "E14",
          "8"
        ],
        [
          "F14",
          "17"
        ],
        [
          "G14",
          "0.48"
        ],
        [
          "H14",
          "0.08"
        ],
        [
          "I14",
          "19"
        ],
        [
          "J14",
          "0.56000000000000005"
        ],
        [
          "K14",
          "0.11"
        ],
        [
          "L14",
          "R7"
        ],
        [
          "M14",
          "Not Industry"
        ],
        [
          "N14",
          "1"
        ],
        [
          "O14",
          "0"
        ],
        [
          "P14",
          "1"
        ],
        [
          "Q14",
          "1"
        ]
      ],
      [
        [
          "A15",
          "schwarz 2019"
        ],
        [
          "B15",
          "Cutometer MPA 580"
        ],
        [
          "C15",
          "0.3"
        ],
        [
          "D15",
          "Unitless"
        ],
        [
          "E15",
          "12"
        ],
        [
          "F15",
          "58"
        ],
        [
          "G15",
          "0.4"
        ],
        [
          "H15",
          "0.08"
        ],
        [
          "I15",
          "55"
        ],
        [
          "J15",
          "0.34"
        ],
        [
          "K15",
          "0.15"
        ],
        [
          "L15",
          "R5"
        ],
        [
          "M15",
          "industry"
        ],
        [
          "N15",
          "1"
        ],
        [
          "O15",
          "0"
        ],
        [
          "P15",
          "1"
        ],
        [
          "Q15",
          "1"
        ]
      ],
      [
        [
          "A16",
          "shugihara2015"
        ],
        [
          "B16",
          "Cutometer 575"
        ],
        [
          "C16",
          "2.5"
        ],
        [
          "D16",
          "Unitless"
        ],
        [
          "E16",
          "8"
        ],
        [
          "F16",
          "26"
        ],
        [
          "G16",
          "0.75800000000000001"
        ],
        [
          "H16",
          "0.109"
        ],
        [
          "I16",
          "27"
        ],
        [
          "J16",
          "0.69699999999999995"
        ],
        [
          "K16",
          "8.6999999999999994E-2"
        ],
        [
          "L16",
          "R2"
        ],
        [
          "M16",
          "industry"
        ],
        [
          "N16",
          "1"
        ],
        [
          "O16",
          "0"
        ],
        [
          "P16",
          "1"
        ],
        [
          "Q16",
          "1"
        ]
      ],
      [
        [
          "A17",
          "Yoon 2014"
        ],
        [
          "B17",
          "Cutometer MPA 580"
        ],
        [
          "C17",
          "0.75"
        ],
        [
          "D17",
          "Unitless"
        ],
        [
          "E17",
          "12"
        ],
        [
          "F17",
          "22"
        ],
        [
          "G17",
          "0.62506443999999994"
        ],
        [
          "H17",
          "5.5599999999999997E-2"
        ],
        [
          "I17",
          "22"
        ],
        [
          "J17",
          "0.62564875999999991"
        ],
        [
          "K17",
          "6.7500000000000004E-2"
        ],
        [
          "L17",
          "R2"
        ],
        [
          "M17",
          "industry"
        ],
        [
          "N17",
          "1"
        ],
        [
          "O17",
          "0"
        ],
        [
          "P17",
          "1"
        ],
        [
          "Q17",
          "1"
        ],
        [
          "R17",
          "PERCENTAGE BOLLOX - same stupid error"
        ]
      ],
      [
        [
          "A18",
          "zmitek 2020"
        ],
        [
          "B18",
          "SkinLab USB Elasticity Module"
        ],
        [
          "C18",
          "4"
        ],
        [
          "D18",
          "Mpa"
        ],
        [
          "E18",
          "12"
        ],
        [
          "F18",
          "16"
        ],
        [
          "G18",
          "1.38"
        ],
        [
          "H18",
          "0.33"
        ],
        [
          "I18",
          "15"
        ],
        [
          "J18",
          "1.59"
        ],
        [
          "K18",
          "0.68"
        ],
        [
          "L18",
          "Mpa"
        ],
        [
          "M18",
          "Not Industry"
        ],
        [
          "N18",
          "0"
        ],
        [
          "O18",
          "1"
        ],
        [
          "P18",
          "1"
        ],
        [
          "Q18",
          "1"
        ]
      ],
      [
        [
          "A19",
          "tak 2021"
        ],
        [
          "B19",
          "Cutometer MPA580"
        ],
        [
          "C19",
          "1"
        ],
        [
          "D19",
          "mm"
        ],
        [
          "E19",
          "12"
        ],
        [
          "F19",
          "36"
        ],
        [
          "G19",
          "0.33"
        ],
        [
          "H19",
          "0.1"
        ],
        [
          "I19",
          "38"
        ],
        [
          "J19",
          "0.36"
        ],
        [
          "K19",
          "0.09"
        ],
        [
          "M19",
          "Not Industry"
        ],
        [
          "N19",
          "1"
        ],
        [
          "O19",
          "0"
        ],
        [
          "P19",
          "0"
        ],
        [
          "Q19",
          "1"
        ]
      ]
    ]
  },
  "hydrodata.xlsx": {
    "xl/worksheets/sheet1.xml": [
      [
        [
          "A1",
          "study"
        ],
        [
          "B1",
          "method"
        ],
        [
          "C1",
          "dose"
        ],
        [
          "D1",
          "unit"
        ],
        [
          "E1",
          "Time"
        ],
        [
          "F1",
          "NE"
        ],
        [
          "G1",
          "ME"
        ],
        [
          "H1",
          "SE"
        ],
        [
          "I1",
          "NC"
        ],
        [
          "J1",
          "MC"
        ],
        [
          "K1",
          "SC"
        ],
        [
          "L1",
          "Notes"
        ],
        [
          "M1",
          "sponsor"
        ],
        [
          "N1",
          "ma1"
        ],
        [
          "O1",
          "ma2"
        ],
        [
          "P1",
          "cap_method"
        ]
      ],
      [
        [
          "A2",
          "Bolke 2019"
        ],
        [
          "B2",
          "CM 825"
        ],
        [
          "C2",
          "2.5"
        ],
        [
          "D2",
          "AU"
        ],
        [
          "E2",
          "12"
        ],
        [
          "F2",
          "36"
        ],
        [
          "G2",
          "44.5"
        ],
        [
          "H2",
          "4.4000000000000004"
        ],
        [
          "I2",
          "36"
        ],
        [
          "J2",
          "36.6"
        ],
        [
          "K2",
          "5.7"
        ],
        [
          "L2",
          "Not micrometer"
        ],
        [
          "M2",
          "Industry"
        ],
        [
          "N2",
          "1"
        ],
        [
          "O2",
          "1"
        ],
        [
          "P2",
          "1"
        ]
      ],
      [
        [
          "A3",
          "Inoue 2016"
        ],
        [
          "B3",
          "CM820"
        ],
        [
          "C3",
          "5"
        ],
        [
          "E3",
          "8"
        ],
        [
          "F3",
          "54"
        ],
        [
          "G3",
          "33.53"
        ],
        [
          "H3",
          "12.42"
        ],
        [
          "I3",
          "26"
        ],
        [
          "J3",
          "25.42"
        ],
        [
          "K3",
          "14.88"
        ],
        [
          "M3",
          "industry"
        ],
        [
          "N3",
          "1"
        ],
        [
          "O3",
          "1"
        ],
        [
          "P3",
          "1"
        ]
      ],
      [
        [
          "A4",
          "Nometo 2020"
        ],
        [
          "B4",
          "CM825"
        ],
        [
          "C4",
          "10"
        ],
        [
          "E4",
          "8"
        ],
        [
          "F4",
          "20"
        ],
        [
          "G4",
          "51.7"
        ],
        [
          "H4",
          "6.5"
        ],
        [
          "I4",
          "19"
        ],
        [
          "J4",
          "41.4"
        ],
        [
          "K4",
          "9.8000000000000007"
        ],
        [
          "M4",
          "industry"
        ],
        [
          "N4",
          "1"
        ],
        [
          "O4",
          "1"
        ],
        [
          "P4",
          "1"
        ]
      ],
      [
        [
          "A5",
          "Ito 2018"
        ],
        [
          "B5",
          "CM Series"
        ],
        [
          "C5",
          "10"
        ],
        [
          "E5",
          "8"
        ],
        [
          "F5",
          "9"
        ],
        [
          "G5",
          "60.3"
        ],
        [
          "H5",
          "8.1999999999999993"
        ],
        [
          "I5",
          "11"
        ],
        [
          "J5",
          "60.9"
        ],
        [
          "K5",
          "10.7"
        ],
        [
          "M5",
          "industry"
        ],
        [
          "N5",
          "1"
        ],
        [
          "O5",
          "1"
        ],
        [
          "P5",
          "1"
        ]
      ],
      [
        [
          "A6",
          "koizuma 2018"
        ],
        [
          "B6",
          "CM825"
        ],
        [
          "C6",
          "3"
        ],
        [
          "E6",
          "12"
        ],
        [
          "F6",
          "37"
        ],
        [
          "G6",
          "55.237000000000002"
        ],
        [
          "H6",
          "9.1669999999999998"
        ],
        [
          "I6",
          "34"
        ],
        [
          "J6",
          "51.58"
        ],
        [
          "K6",
          "7.7"
        ],
        [
          "L6",
          "Used SEM, needed correction; assumption made of first order delta correction for conversion "
        ],
        [
          "M6",
          "industry"
        ],
        [
          "N6",
          "1"
        ],
        [
          "O6",
          "1"
        ],
        [
          "P6",
          "1"
        ]
      ],
      [
        [
          "A7",
          "shugihara2015"
        ],
        [
          "B7",
          "CM820"
        ],
        [
          "C7",
          "2.5"
        ],
        [
          "E7",
          "8"
        ],
        [
          "F7",
          "26"
        ],
        [
          "G7",
          "73.98"
        ],
        [
          "H7",
          "8.86"
        ],
        [
          "I7",
          "27"
        ],
        [
          "J7",
          "70.849999999999994"
        ],
        [
          "K7",
          "10.199999999999999"
        ],
        [
          "M7",
          "industry"
        ],
        [
          "N7",
          "1"
        ],
        [
          "O7",
          "1"
        ],
        [
          "P7",
          "1"
        ]
      ],
      [
        [
          "A8",
          "schwarz 2019"
        ],
        [
          "B8",
          "MoistureMeterSC"
        ],
        [
          "C8",
          "0.3"
        ],
        [
          "E8",
          "12"
        ],
        [
          "F8",
          "58"
        ],
        [
          "G8",
          "61.31"
        ],
        [
          "H8",
          "24.53"
        ],
        [
          "I8",
          "55"
        ],
        [
          "J8",
          "62.08"
        ],
        [
          "K8",
          "28.41"
        ],
        [
          "M8",
          "industry"
        ],
        [
          "N8",
          "1"
        ],
        [
          "O8",
          "1"
        ],
        [
          "P8",
          "1"
        ]
      ],
      [
        [
          "A9",
          "zmitek 2020"
        ],
        [
          "B9",
          "SkinLab Combo hydration probe"
        ],
        [
          "C9",
          "4"
        ],
        [
          "E9",
          "12"
        ],
        [
          "F9",
          "16"
        ],
        [
          "G9",
          "200"
        ],
        [
          "H9",
          "52.3"
        ],
        [
          "I9",
          "15"
        ],
        [
          "J9",
          "194.1"
        ],
        [
          "K9",
          "44.4"
        ],
        [
          "L9",
          "not cap"
        ],
        [
          "M9",
          "Not Industry"
        ],
        [
          "N9",
          "1"
        ],
        [
          "O9",
          "1"
        ],
        [
          "P9",
          "0"
        ]
      ],
      [
        [
          "A10",
          "choi 2014"
        ],
        [
          "B10",
          "CM Series"
        ],
        [
          "C10",
          "3"
        ],
        [
          "E10",
          "12"
        ],
        [
          "F10",
          "8"
        ],
        [
          "G10",
          "71.09"
        ],
        [
          "H10",
          "13.22"
        ],
        [
          "I10",
          "8"
        ],
        [
          "J10",
          "61.3"
        ],
        [
          "K10",
          "10.48"
        ],
        [
          "M10",
          "Not Industry"
        ],
        [
          "N10",
          "1"
        ],
        [
          "O10",
          "1"
        ],
        [
          "P10",
          "1"
        ]
      ],
      [
        [
          "A11",
          "Asserin 2015"
        ],
        [
          "B11",
          "CM Device "
        ],
        [
          "C11",
          "10"
        ],
        [
          "D11",
          "AU"
        ],
        [
          "E11",
          "8"
        ],
        [
          "F11",
          "11"
        ],
        [
          "G11",
          "65.28"
        ],
        [
          "H11",
          "5.9329999999999998"
        ],
        [
          "I11",
          "11"
        ],
        [
          "J11",
          "52.91"
        ],
        [
          "K11",
          "6.5670000000000002"
        ],
        [
          "L11",
          "Extracted from fig? 2x"
        ],
        [
          "M11",
          "Industry"
        ],
        [
          "N11",
          "0"
        ],
        [
          "O11",
          "1"
        ],
        [
          "P11",
          "1"
        ]
      ],
      [
        [
          "A12",
          "di cerbo 2015"
        ],
        [
          "B12",
          "skin tester device"
        ],
        [
          "C12",
          "0.248"
        ],
        [
          "D12",
          "Unclear"
        ],
        [
          "E12",
          "2"
        ],
        [
          "F12",
          "15"
        ],
        [
          "G12",
          "61.98"
        ],
        [
          "H12",
          "13.04420791002658"
        ],
        [
          "I12",
          "15"
        ],
        [
          "J12",
          "55.56"
        ],
        [
          "K12",
          "13.04420791002658"
        ],
        [
          "L12",
          "Used SEM, needed correction"
        ],
        [
          "M12",
          "Not Industry"
        ],
        [
          "N12",
          "0"
        ],
        [
          "O12",
          "1"
        ],
        [
          "P12",
          "0"
        ]
      ],
      [
        [
          "A13",
          "jung 2020"
        ],
        [
          "B13",
          "Moisture Meter D"
        ],
        [
          "C13",
          "1"
        ],
        [
          "D13",
          "percent"
        ],
        [
          "E13",
          "12"
        ],
        [
          "F13",
          "24"
        ],
        [
          "G13",
          "42.03"
        ],
        [
          "H13",
          "8.6"
        ],
        [
          "I13",
          "20"
        ],
        [
          "J13",
          "39.82"
        ],
        [
          "K13",
          "6.67"
        ],
        [
          "L13",
          "mixed up percentages"
        ],
        [
          "M13",
          "industry"
        ],
        [
          "N13",
          "0"
        ],
        [
          "O13",
          "1"
        ],
        [
          "P13",
          "1"
        ]
      ],
      [
        [
          "A14",
          "kim 2018"
        ],
        [
          "B14",
          "CM 825"
        ],
        [
          "C14",
          "1"
        ],
        [
          "D14",
          "AU"
        ],
        [
          "E14",
          "12"
        ],
        [
          "F14",
          "33"
        ],
        [
          "G14",
          "61.14"
        ],
        [
          "H14",
          "11.31"
        ],
        [
          "I14",
          "31"
        ],
        [
          "J14",
          "53.02"
        ],
        [
          "K14",
          "13.59"
        ],
        [
          "M14",
          "industry"
        ],
        [
          "N14",
          "0"
        ],
        [
          "O14",
          "1"
        ],
        [
          "P14",
          "1"
        ]
      ],
      [
        [
          "A15",
          "miyanga 2021"
        ],
        [
          "B15",
          "CM device "
        ],
        [
          "C15",
          "5"
        ],
        [
          "D15",
          "AU"
        ],
        [
          "E15",
          "12"
        ],
        [
          "F15",
          "33"
        ],
        [
          "G15",
          "32.354096599999998"
        ],
        [
          "H15",
          "7.8362239999999996"
        ],
        [
          "I15",
          "33"
        ],
        [
          "J15",
          "29.7682"
        ],
        [
          "K15",
          "7.937229499999999"
        ],
        [
          "L15",
          "percentage nonsese"
        ],
        [
          "M15",
          "industry"
        ],
        [
          "N15",
          "0"
        ],
        [
          "O15",
          "1"
        ],
        [
          "P15",
          "1"
        ],
        [
          "Q15",
          "not"
        ]
      ],
      [
        [
          "A16",
          "tak 2021"
        ],
        [
          "B16",
          "CM 825"
        ],
        [
          "C16",
          "1"
        ],
        [
          "E16",
          "12"
        ],
        [
          "F16",
          "36"
        ],
        [
          "G16",
          "212.1"
        ],
        [
          "H16",
          "16"
        ],
        [
          "I16",
          "38"
        ],
        [
          "J16",
          "209"
        ],
        [
          "K16",
          "18.7"
        ],
        [
          "M16",
          "industry"
        ],
        [
          "N16",
          "0"
        ],
        [
          "O16",
          "1"
        ],
        [
          "P16",
          "1"
        ],
        [
          "Q16",
          "not"
        ]
      ],
      [
        [
          "A17",
          "yoon 2014"
        ],
        [
          "B17",
          "CM Series"
        ],
        [
          "C17",
          "0.75"
        ],
        [
          "E17",
          "12"
        ],
        [
          "F17",
          "22"
        ],
        [
          "G17",
          "62.289000000000001"
        ],
        [
          "H17",
          "16.875"
        ],
        [
          "I17",
          "22"
        ],
        [
          "J17",
          "57.382999999999996"
        ],
        [
          "K17",
          "15.293999999999999"
        ],
        [
          "L17",
          "original paper not sig, sig in analysis because authors fuck up %"
        ],
        [
          "M17",
          "industry"
        ],
        [
          "N17",
          "0"
        ],
        [
          "O17",
          "1"
        ],
        [
          "P17",
          "1"
        ],
        [
          "Q17",
          "not"
        ]
      ]
    ]
  }
}

Öppna källan

Full current exact imported definitions of distinct collagen and gelatin records; collagen definition empty, not invented; no merge or reopened profile history

{"id":"chebi-3815","version_id":"d1ecb840-fc4a-4e9d-a25c-49379abcbfd4","name":"Collagen","definition":""}

{"id":"chebi-5291","version_id":"0104843d-dd76-4f6e-b71b-30c8717dfedf","name":"gelatin","definition":"A mixture of peptides and proteins produced by partial hydrolysis of collagen extracted from the skin, bones, and connective tissues of animals such as domesticated cattle, chicken, pigs, and fish."}

Läs produktdeklarationen på ActiQs

Mikroniserat kreatinmonohydrat, hydrolyserat bovint kollagen, majsstärkelse, surhetsreglerande medel (citronsyra), aromer, sötningsmedel (sukralos, acesulfam K), färgämne (rödbetsrött).

Innehåll	1 skopa (10 g)	2 skopor (20 g)
Mikroniserat kreatin	3 g	6 g
Hydrolyserat kollagen	3 g	6 g

Läs produktdeklarationen på ActiQs

100% kollagen. Råvaran har utvunnits från vit matfisk.

Läs produktdeklarationen på ActiQs

Ecolovita ASC- certifierat marint (FISK) kollagen protein-peptider (Vietnam), askorbinsyra, zinkcitrat, arom (naturlig citron).
Förvaring:
Förvaras utom räckhåll för barn i väl försluten originalförpackning.
Övrig information:
Detta är ett kosttillskott och bör ej användas som ett alternativ till en varierad kost. Den rekommenderade dagliga dosen bör ej överskridas. Förvaras oåtkomlig för små barn. Tänk på vikten av en mångsidig och balanserad kost och en hälsosam livsstil. Produkten är avsedd för friska personer över 18 år. Om Du är gravid, ammande, lider av sjukdom eller behandlas med läkemedel bör Du kontakta läkare innan Du använder produkten.

Innehåll:	1 påse	DRI % *
Ecolovita	5 000 mg	
Vitamin C	80 mg	100
Zink	5 mg	100

Läs produktdeklarationen på ActiQs

Hydrolyserat kollagen (typ 1, bovint ursprung).