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Obesity, Glycemic Traits, Lifestyle Factors, and Risk of Facial Aging: A Mendelian Randomization Study in 423,999 Participants

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Abstract

Background

Several recent observational studies have associated obesity, lifestyle factors (smoking, sleep duration, and alcohol drinking), and glycemic traits with facial aging. However, whether this relationship is causal due to confounding and reverse causation is yet to be substantiated.

Aims

We aimed to assess these relationships using Mendelian randomization (MR).

Methods

For the instrumental variables, this paper selected independent single nucleotide polymorphisms (SNPs) linked to the exposures at a genome-wide state (P < 5 × 10−8) in equivalent genome-wide association studies (GWAS). Using the UK Biobank, we obtained summary-level data for facial aging on 423,999 individuals. The primary assessments were performed through the combination of complementing techniques (simple method approaches, weighted model, MR-Egger, and weighted median) and the inverse-variance-weighted method. Along with that, we examined the heterogeneity and horizontal pleiotropy through different types of sensitivity analyses.

Results

The correlations were (a) facial aging for body mass index (BMI, OR = 1.054, 95% CI 1.044–1.64), (b) waist/hip ratio (OR = 1.056, 95% CI 1.023–1.091), and (c) smoking (OR = 1.023, 95% CI 1.007–1.039). Equally important, the correlations for waist/hip ratio remained robust after adjusting for the genetically predicted BMI (OR = 1.028, 95% CI 1.003–1.054). However, no causal effects of alcoholic drinking, glycemic traits, and sleep duration on facial aging were observed.

Conclusions

The outcomes shed light on the potential correlation of obesity and cigarette smoking with facial aging while putting forward a more comprehensive and credible foundation for the optimization of facial aging strategies.

No Level Assigned

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References

  1. Freiman A, Bird G, Metelitsa AI, Barankin B, Lauzon GJ (2004) Cutaneous effects of smoking. J Cutan Med Surg 8(6):415–423. https://doi.org/10.1007/s10227-005-0020-8

    Article  PubMed  Google Scholar 

  2. Cosgrove MC, Franco OH, Granger SP, Murray PG, Mayes AE (2007) Dietary nutrient intakes and skin-aging appearance among middle-aged American women. Am J Clin Nutr 86(4):1225–1231. https://doi.org/10.1093/ajcn/86.4.1225

    Article  CAS  PubMed  Google Scholar 

  3. Wanitphakdeedecha R, Yan C, Ng J, Fundarò S (2021) Absorbable barbed threads for lower facial Soft-Tissue repositioning in asians. Dermatol Ther (Heidelb) 11(4):1395–1408. https://doi.org/10.1007/s13555-021-00569-x

    Article  PubMed  Google Scholar 

  4. Noordam R, Gunn DA, Tomlin CC, Maier AB, Mooijaart SP, Slagboom PE, Westendorp RG, de Craen AJ, van Heemst D (2013) High serum glucose levels are associated with a higher perceived age. Age (Dordr) 35(1):189–195. https://doi.org/10.1007/s11357-011-9339-9

    Article  CAS  PubMed  Google Scholar 

  5. Son HU, Choi HJ, Alam MB, Jeong CG, Lee HI, Kim SL, Zhao P, Kim TH, Lee SH (2021) Prunus mume Seed Exhibits Inhibitory Effect on Skin Senescence via SIRT1 and MMP-1 Regulation. Oxid Med Cell Longev 2021:5528795. https://doi.org/10.1155/2021/5528795

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Zagórska-Dziok M, Ziemlewska A, Nizioł-Łukaszewska Z, Bujak T (2020) Antioxidant activity and cytotoxicity of Medicago sativa L. seeds and herb extract on skin cells. Biores Open Access 9(1):229-242. https://doi.org/10.1089/biores.2020.0015

  7. Gunn DA, Dick JL, van Heemst D, Griffiths CE, Tomlin CC, Murray PG, Griffiths TW, Ogden S, Mayes AE, Westendorp RG, Slagboom PE, de Craen AJ (2015) Lifestyle and youthful looks. Br J Dermatol 172(5):1338–1345. https://doi.org/10.1111/bjd.13646

    Article  CAS  PubMed  Google Scholar 

  8. Deng Y, Li Q, Zhou F, Li G, Liu J, Lv J, Li L, Chang D (2022) Telomere length and the risk of cardiovascular diseases: A Mendelian randomization study. Front Cardiovasc Med 9:1012615. https://doi.org/10.3389/fcvm.2022.1012615

    Article  PubMed  PubMed Central  Google Scholar 

  9. Brumpton B, Sanderson E, Heilbron K, Hartwig FP, Harrison S, Vie GÅ, Cho Y, Howe LD, Hughes A, Boomsma DI, Havdahl A, Hopper J, Neale M, Nivard MG, Pedersen NL, Reynolds CA, Tucker-Drob EM, Grotzinger A, Howe L, Morris T, Li S, Auton A, Windmeijer F, Chen WM, Bjørngaard JH, Hveem K, Willer C, Evans DM, Kaprio J, Davey SG, Åsvold BO, Hemani G, Davies NM (2020) Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses. Nat Commun 11(1):3519. https://doi.org/10.1038/s41467-020-17117-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Millwood IY, Bennett DA, Holmes MV, Boxall R, Guo Y, Bian Z, Yang L, Sansome S, Chen Y, Du H, Yu C, Hacker A, Reilly DF, Tan Y, Hill MR, Chen J, Peto R, Shen H, Collins R, Clarke R, Li L, Walters RG, Chen Z (2018) Association of CETP gene variants with risk for vascular and nonvascular diseases among chinese adults. Jama Cardiol 3(1):34–43. https://doi.org/10.1001/jamacardio.2017.4177

    Article  PubMed  Google Scholar 

  11. Larsson SC, Burgess S (2022) Appraising the causal role of smoking in multiple diseases: A systematic review and meta-analysis of Mendelian randomization studies. eBiomedicine 82:104154. https://doi.org/10.1016/j.ebiom.2022.104154

  12. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, Downey P, Elliott P, Green J, Landray M, Liu B, Matthews P, Ong G, Pell J, Silman A, Young A, Sprosen T, Peakman T, Collins R (2015) UK Biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12(3):e1001779. https://doi.org/10.1371/journal.pmed.1001779

  13. Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR (2018) Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum Mol Genet 27(20):3641–3649. https://doi.org/10.1093/hmg/ddy271

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T (2015) New genetic loci link adipose and insulin biology to body fat distribution. Nature 518(7538):187–196. https://doi.org/10.1038/nature14132

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Liu M, Jiang Y, Wedow R, Li Y, Brazel DM, Chen F, Datta G, Davila-Velderrain J, McGuire D, Tian C, Zhan X, 23 and Me Research Team, HUNT All-In Psychiatry, Choquet H, Docherty AR, Faul JD, Foerster JR, Fritsche LG, Gabrielsen ME, Gordon SD, Haessler J, Hottenga JJ, Huang H, Jang SK, Jansen PR, Ling Y, Mägi R, Matoba N, McMahon G, Mulas A, Orrù V, Palviainen T, Pandit A, Reginsson GW, Skogholt AH, Smith JA, Taylor AE, Turman C, Willemsen G, Young H, Young KA, Zajac GJM, Zhao W, Zhou W, Bjornsdottir G, Boardman JD, Boehnke M, Boomsma DI, Chen C, Cucca F, Davies GE, Eaton CB, Ehringer MA, Esko T, Fiorillo E, Gillespie NA, Gudbjartsson DF, Haller T, Harris KM, Heath AC, Hewitt JK, Hickie IB, Hokanson JE, Hopfer CJ, Hunter DJ, Iacono WG, Johnson EO, Kamatani Y, Kardia SLR, Keller MC, Kellis M, Kooperberg C, Kraft P, Krauter KS, Laakso M, Lind PA, Loukola A, Lutz SM, Madden PAF, Martin NG, McGue M, McQueen MB, Medland SE, Metspalu A, Mohlke KL, Nielsen JB, Okada Y, Peters U, Polderman TJC, Posthuma D, Reiner AP, Rice JP, Rimm E, Rose RJ, Runarsdottir V, Stallings MC, Stančáková A, Stefansson H, Thai KK, Tindle HA, Tyrfingsson T, Wall TL, Weir DR, Weisner C, Whitfield JB, Winsvold BS, Yin J, Zuccolo L, Bierut LJ, Hveem K, Lee JJ, Munafò MR, Saccone NL, Willer CJ, Cornelis MC, David SP, Hinds DA, Jorgenson E, Kaprio J, Stitzel JA, Stefansson K, Thorgeirsson TE, Abecasis G, Liu DJ, Vrieze S (2019) Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet 51(2):237–244. https://doi.org/10.1038/s41588-018-0307-5

  16. Chen J, Spracklen CN, Marenne G, Varshney A, Corbin LJ, Luan J, Willems SM, Wu Y, Zhang X, Horikoshi M, Boutin TS, Mägi R, Waage J, Li-Gao R, Chan KHK, Yao J, Anasanti MD, Chu AY, Claringbould A, Heikkinen J, Hong J, Hottenga JJ, Huo S, Kaakinen MA, Louie T, März W, Moreno-Macias H, Ndungu A, Nelson SC, Nolte IM, North KE, Raulerson CK, Ray D, Rohde R, Rybin D, Schurmann C, Sim X, Southam L, Stewart ID, Wang CA, Wang Y, Wu P, Zhang W, Ahluwalia TS, Appel EVR, Bielak LF, Brody JA, Burtt NP, Cabrera CP, Cade BE, Chai JF, Chai X, Chang LC, Chen CH, Chen BH, Chitrala KN, Chiu YF, de Haan HG, Delgado GE, Demirkan A, Duan Q, Engmann J, Fatumo SA, Gayán J, Giulianini F, Gong JH, Gustafsson S, Hai Y, Hartwig FP, He J, Heianza Y, Huang T, Huerta-Chagoya A, Hwang MY, Jensen RA, Kawaguchi T, Kentistou KA, Kim YJ, Kleber ME, Kooner IK, Lai S, Lange LA, Langefeld CD, Lauzon M, Li M, Ligthart S, Liu J, Loh M, Long J, Lyssenko V, Mangino M, Marzi C, Montasser ME, Nag A, Nakatochi M, Noce D, Noordam R, Pistis G, Preuss M, Raffield L, Rasmussen-Torvik LJ, Rich SS, Robertson NR, Rueedi R, Ryan K, Sanna S, Saxena R, Schraut KE, Sennblad B, Setoh K, Smith AV, Sparsø T, Strawbridge RJ, Takeuchi F, Tan J, Trompet S, van den Akker E, van der Most PJ, Verweij N, Vogel M, Wang H, Wang C, Wang N, Warren HR, Wen W, Wilsgaard T, Wong A, Wood AR, Xie T, Zafarmand MH, Zhao JH, Zhao W, Amin N, Arzumanyan Z, Astrup A, Bakker SJL, Baldassarre D, Beekman M, Bergman RN, Bertoni A, Blüher M, Bonnycastle LL, Bornstein SR, Bowden DW, Cai Q, Campbell A, Campbell H, Chang YC, de Geus EJC, Dehghan A, Du S, Eiriksdottir G, Farmaki AE, Frånberg M, Fuchsberger C, Gao Y, Gjesing AP, Goel A, Han S, Hartman CA, Herder C, Hicks AA, Hsieh CH, Hsueh WA, Ichihara S, Igase M, Ikram MA, Johnson WC, Jørgensen ME, Joshi PK, Kalyani RR, Kandeel FR, Katsuya T, Khor CC, Kiess W, Kolcic I, Kuulasmaa T, Kuusisto J, Läll K, Lam K, Lawlor DA, Lee NR, Lemaitre RN, Li H, Lifelines Cohort Study, Lin SY, Lindström J, Linneberg A, Liu J, Lorenzo C, Matsubara T, Matsuda F, Mingrone G, Mooijaart S, Moon S, Nabika T, Nadkarni GN, Nadler JL, Nelis M, Neville MJ, Norris JM, Ohyagi Y, Peters A, Peyser PA, Polasek O, Qi Q, Raven D, Reilly DF, Reiner A, Rivideneira F, Roll K, Rudan I, Sabanayagam C, Sandow K, Sattar N, Schürmann A, Shi J, Stringham HM, Taylor KD, Teslovich TM, Thuesen B, Timmers PRHJ, Tremoli E, Tsai MY, Uitterlinden A, van Dam RM, van Heemst D, van Hylckama Vlieg A, van Vliet-Ostaptchouk JV, Vangipurapu J, Vestergaard H, Wang T, Willems van Dijk K, Zemunik T, Abecasis GR, Adair LS, Aguilar-Salinas CA, Alarcón-Riquelme ME, An P, Aviles-Santa L, Becker DM, Beilin LJ, Bergmann S, Bisgaard H, Black C, Boehnke M, Boerwinkle E, Böhm BO, Bønnelykke K, Boomsma DI, Bottinger EP, Buchanan TA, Canouil M, Caulfield MJ, Chambers JC, Chasman DI, Chen YDI, Cheng CY, Collins FS, Correa A, Cucca F, de Silva HJ, Dedoussis G, Elmståhl S, Evans MK, Ferrannini E, Ferrucci L, Florez JC, Franks PW, Frayling TM, Froguel P, Gigante B, Goodarzi MO, Gordon-Larsen P, Grallert H, Grarup N, Grimsgaard S, Groop L, Gudnason V, Guo X, Hamsten A, Hansen T, Hayward C, Heckbert SR, Horta BL, Huang W, Ingelsson E, James PS, Jarvelin MR, Jonas JB, Jukema JW, Kaleebu P, Kaplan R, Kardia SLR, Kato N, Keinanen-Kiukaanniemi SM, Kim BJ, Kivimaki M, Koistinen HA, Kooner JS, Körner A, Kovacs P, Kuh D, Kumari M, Kutalik Z, Laakso M, Lakka TA, Launer LJ, Leander K, Li H, Lin X, Lind L, Lindgren C, Liu S, Loos RJF, Magnusson PKE, Mahajan A, Metspalu A, Mook-Kanamori DO, Mori TA, Munroe PB, Njølstad I, O'Connell JR, Oldehinkel AJ, Ong KK, Padmanabhan S, Palmer CNA, Palmer ND, Pedersen O, Pennell CE, Porteous DJ, Pramstaller PP, Province MA, Psaty BM, Qi L, Raffel LJ, Rauramaa R, Redline S, Ridker PM, Rosendaal FR, Saaristo TE, Sandhu M, Saramies J, Schneiderman N, Schwarz P, Scott LJ, Selvin E, Sever P, Shu XO, Slagboom PE, Small KS, Smith BH, Snieder H, Sofer T, Sørensen TIA, Spector TD, Stanton A, Steves CJ, Stumvoll M, Sun L, Tabara Y, Tai ES, Timpson NJ, Tönjes A, Tuomilehto J, Tusie T, Uusitupa M, van der Harst P, van Duijn C, Vitart V, Vollenweider P, Vrijkotte TGM, Wagenknecht LE, Walker M, Wang YX, Wareham NJ, Watanabe RM, Watkins H, Wei WB, Wickremasinghe AR, Willemsen G, Wilson JF, Wong TY, Wu JY, Xiang AH, Yanek LR, Yengo L, Yokota M, Zeggini E, Zheng W, Zonderman AB, Rotter JI, Gloyn AL, McCarthy MI, Dupuis J, Meigs JB, Scott RA, Prokopenko I, Leong A, Liu CT, Parker SCJ, Mohlke KL, Langenberg C, Wheeler E, Morris AP, Barroso I, The Meta-Analysis of Glucose and Insulin-related Traits Consortium (2021) The trans-ancestral genomic architecture of glycemic traits. Nat Genet 53(6):840–860. https://doi.org/10.1038/s41588-021-00852-9

  17. Soranzo N, Sanna S, Wheeler E, Gieger C, Radke D, Dupuis J, Bouatia-Naji N, Langenberg C, Prokopenko I, Stolerman E, Sandhu MS, Heeney MM, Devaney JM, Reilly MP, Ricketts SL, Stewart AF, Voight BF, Willenborg C, Wright B, Altshuler D, Arking D, Balkau B, Barnes D, Boerwinkle E, Böhm B, Bonnefond A, Bonnycastle LL, Boomsma DI, Bornstein SR, Böttcher Y, Bumpstead S, Burnett-Miller MS, Campbell H, Cao A, Chambers J, Clark R, Collins FS, Coresh J, de Geus EJ, Dei M, Deloukas P, Döring A, Egan JM, Elosua R, Ferrucci L, Forouhi N, Fox CS, Franklin C, Franzosi MG, Gallina S, Goel A, Graessler J, Grallert H, Greinacher A, Hadley D, Hall A, Hamsten A, Hayward C, Heath S, Herder C, Homuth G, Hottenga JJ, Hunter-Merrill R, Illig T, Jackson AU, Jula A, Kleber M, Knouff CW, Kong A, Kooner J, Köttgen A, Kovacs P, Krohn K, Kühnel B, Kuusisto J, Laakso M, Lathrop M, Lecoeur C, Li M, Li M, Loos RJ, Luan J, Lyssenko V, Mägi R, Magnusson PK, Mälarstig A, Mangino M, Martínez-Larrad MT, März W, Mcardle WL, Mcpherson R, Meisinger C, Meitinger T, Melander O, Mohlke KL, Mooser VE, Morken MA, Narisu N, Nathan DM, Nauck M, O’Donnell C, Oexle K, Olla N, Pankow JS, Payne F, Peden JF, Pedersen NL, Peltonen L, Perola M, Polasek O, Porcu E, Rader DJ, Rathmann W, Ripatti S, Rocheleau G, Roden M, Rudan I, Salomaa V, Saxena R, Schlessinger D, Schunkert H, Schwarz P, Seedorf U, Selvin E, Serrano-Ríos M, Shrader P, Silveira A, Siscovick D, Song K, Spector TD, Stefansson K, Steinthorsdottir V, Strachan DP, Strawbridge R, Stumvoll M, Surakka I, Swift AJ, Tanaka T, Teumer A, Thorleifsson G, Thorsteinsdottir U, Tönjes A, Usala G, Vitart V, Völzke H, Wallaschofski H, Waterworth DM, Watkins H, Wichmann HE, Wild SH, Willemsen G, Williams GH, Wilson JF, Winkelmann J, Wright AF, Zabena C, Zhao JH, Epstein SE, Erdmann J, Hakonarson HH, Kathiresan S, Khaw KT, Roberts R, Samani NJ, Fleming MD, Sladek R, Abecasis G, Boehnke M, Froguel P, Groop L, Mccarthy MI, Kao WH, Florez JC, Uda M, Wareham NJ, Barroso I, Meigs JB (2010) Common variants at 10 genomic loci influence hemoglobin a1(C) levels via glycemic and nonglycemic pathways. Diabetes 59(12):3229–3239. https://doi.org/10.2337/db10-0502

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Yuan S, Larsson SC (2022) Adiposity, diabetes, lifestyle factors and risk of gastroesophageal reflux disease: a Mendelian randomization study. Eur J Epidemiol 37(7):747–754. https://doi.org/10.1007/s10654-022-00842-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhan Y, Hägg S (2021) Association between genetically predicted telomere length and facial skin aging in the UK Biobank: A Mendelian randomization study. Geroscience 43(3):1519–1525. https://doi.org/10.1007/s11357-020-00283-0

    Article  CAS  PubMed  Google Scholar 

  20. Jiang L, Zheng Z, Qi T, Kemper KE, Wray NR, Visscher PM, Yang J (2019) A resource-efficient tool for mixed model association analysis of large-scale data. Nat Genet 51(12):1749–1755. https://doi.org/10.1038/s41588-019-0530-8

    Article  CAS  PubMed  Google Scholar 

  21. Clarke L, Zheng-Bradley X, Smith R, Kulesha E, Xiao C, Toneva I, Vaughan B, Preuss D, Leinonen R, Shumway M, Sherry S, Flicek P (2012) The 1000 Genomes Project: Data management and community access. Nat Methods 9(5):459–462. https://doi.org/10.1038/nmeth.1974

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Machiela MJ, Chanock SJ (2015) LDlink: A web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics 31(21):3555–3557. https://doi.org/10.1093/bioinformatics/btv402

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Burgess S, Bowden J, Fall T, Ingelsson E, Thompson SG (2017) Sensitivity analyses for robust causal inference from mendelian randomization analyses with multiple genetic variants. Epidemiology 28(1):30–42. https://doi.org/10.1097/EDE.0000000000000559

    Article  PubMed  Google Scholar 

  24. Cho Y, Haycock PC, Sanderson E, Gaunt TR, Zheng J, Morris AP, Davey SG, Hemani G (2020) Exploiting horizontal pleiotropy to search for causal pathways within a Mendelian randomization framework. Nat Commun 11(1):1010. https://doi.org/10.1038/s41467-020-14452-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bowden J, Davey SG, Haycock PC, Burgess S (2016) Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 40(4):304–314. https://doi.org/10.1002/gepi.21965

    Article  PubMed  PubMed Central  Google Scholar 

  26. Bowden J, Davey SG, Burgess S (2015) Mendelian randomization with invalid instruments: Effect estimation and bias detection through Egger regression. Int J Epidemiol 44(2):512–525. https://doi.org/10.1093/ije/dyv080

    Article  PubMed  PubMed Central  Google Scholar 

  27. Hartwig FP, Davey SG, Bowden J (2017) Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol 46(6):1985–1998. https://doi.org/10.1093/ije/dyx102

    Article  PubMed  PubMed Central  Google Scholar 

  28. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R, Tan VY, Yarmolinsky J, Shihab HA, Timpson NJ, Evans DM, Relton C, Martin RM, Davey Smith G, Gaunt TR, Haycock PC (2018) The MR-Base platform supports systematic causal inference across the human phenome. Elife 7. https://doi.org/10.7554/eLife.34408

  29. Francès C, Boisnic S, Hartmann DJ, Dautzenberg B, Branchet MC, Charpentier YL, Robert L (1991) Changes in the elastic tissue of the non-sun-exposed skin of cigarette smokers. Br J Dermatol 125(1):43–47. https://doi.org/10.1111/j.1365-2133.1991.tb06037.x

    Article  PubMed  Google Scholar 

  30. Ichibori R, Fujiwara T, Tanigawa T, Kanazawa S, Shingaki K, Torii K, Tomita K, Yano K, Sakai Y, Hosokawa K (2014) Objective assessment of facial skin aging and the associated environmental factors in Japanese monozygotic twins. J Cosmet Dermatol 13(2):158–163. https://doi.org/10.1111/jocd.12081

    Article  PubMed  PubMed Central  Google Scholar 

  31. Rexbye H, Petersen I, Johansens M, Klitkou L, Jeune B, Christensen K (2006) Influence of environmental factors on facial ageing. Age Ageing 35(2):110–115. https://doi.org/10.1093/ageing/afj031

    Article  PubMed  Google Scholar 

  32. Doshi DN, Hanneman KK, Cooper KD (2007) Smoking and skin aging in identical twins. Arch Dermatol 143(12):1543–1546. https://doi.org/10.1001/archderm.143.12.1543

    Article  PubMed  Google Scholar 

  33. Kadunce DP, Burr R, Gress R, Kanner R, Lyon JL, Zone JJ (1991) Cigarette smoking: Risk factor for premature facial wrinkling. Ann Intern Med 114(10):840–844. https://doi.org/10.7326/0003-4819-114-10-840

    Article  CAS  PubMed  Google Scholar 

  34. Ishiwata T, Seyama K, Hirao T, Shimada K, Morio Y, Miura K, Kume A, Takagi H, Takahashi K (2013) Improvement in skin color achieved by smoking cessation. Int J Cosmet Sci 35(2):191–195. https://doi.org/10.1111/ics.12025

    Article  CAS  PubMed  Google Scholar 

  35. Ernster VL, Grady D, Miike R, Black D, Selby J, Kerlikowske K (1995) Facial wrinkling in men and women, by smoking status. Am J Public Health 85(1):78–82. https://doi.org/10.2105/ajph.85.1.78

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lahmann C, Bergemann J, Harrison G, Young AR (2001) Matrix metalloproteinase-1 and skin ageing in smokers. Lancet 357(9260):935–936. https://doi.org/10.1016/S0140-6736(00)04220-3

    Article  CAS  PubMed  Google Scholar 

  37. Wolf R, Tur E, Wolf D, Landau M (1992) The effect of smoking on skin moisture and on surface lipids. Int J Cosmet Sci 14(2):83–88. https://doi.org/10.1111/j.1467-2494.1992.tb00042.x

    Article  CAS  PubMed  Google Scholar 

  38. Nanzadsuren T, Myatav T, Dorjkhuu A, Ganbat M, Batbold C, Batsuuri B, Byamba K (2022) Skin aging risk factors: A nationwide population study in Mongolia risk factors of skin aging. PLoS One 17(1):e249506. https://doi.org/10.1371/journal.pone.0249506

  39. Ezure T, Amano S (2010) Increased subcutaneous adipose tissue impairs dermal function in diet-induced obese mice. Exp Dermatol 19(10):878–882. https://doi.org/10.1111/j.1600-0625.2009.00970.x

    Article  CAS  PubMed  Google Scholar 

  40. Ezure T, Amano S (2010) Influence of subcutaneous adipose tissue mass on dermal elasticity and sagging severity in lower cheek. Skin Res Technol 16(3):332–338. https://doi.org/10.1111/j.1600-0846.2010.00438.x

    Article  PubMed  Google Scholar 

  41. Guyuron B, Rowe DJ, Weinfeld AB, Eshraghi Y, Fathi A, Iamphongsai S (2009) Factors contributing to the facial aging of identical twins. Plast Reconstr Surg 123(4):1321–1331. https://doi.org/10.1097/PRS.0b013e31819c4d42

    Article  CAS  PubMed  Google Scholar 

  42. Hamer MA, Pardo LM, Jacobs LC, Ikram MA, Laven JS, Kayser M, Hollestein LM, Gunn DA, Nijsten T (2017) Lifestyle and physiological factors associated with facial wrinkling in men and women. J Invest Dermatol 137(8):1692–1699. https://doi.org/10.1016/j.jid.2017.04.002

    Article  CAS  PubMed  Google Scholar 

  43. Goodman GD, Kaufman J, Day D, Weiss R, Kawata AK, Garcia JK, Santangelo S, Gallagher CJ (2019) Impact of smoking and alcohol use on facial aging in women: Results of a large multinational, multiracial, cross-sectional survey. J Clin Aesthet Dermatol 12(8):28–39

    PubMed  PubMed Central  Google Scholar 

  44. Dong K, Goyarts E, Rella A, Pelle E, Wong YH, Pernodet N (2020) Age associated decrease of MT-1 melatonin receptor in human dermal skin fibroblasts impairs protection against UV-induced DNA damage. Int J Mol Sci 21(1):326. https://doi.org/10.3390/ijms21010326

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Acknowledgements

Not applicable.

Funding

This study was funded by Key Technologies R & D Program of Henan Province (222102310188).

Author information

Authors and Affiliations

  1. Department of Plastic and Reconstructive Surgery, the First Affiliated Hospital of Zhengzhou University, #1 Jianshe East Road, Zhengzhou, 450052, China

    Xuan-jun Liu, Muhammad Tipu Sultan & Guang-shuai Li

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  1. Xuan-jun Liu
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  2. Muhammad Tipu Sultan
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  3. Guang-shuai Li
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Contributions

LXJ helped in conceptualization; software; data curation; methodology; visualization; writing—original draft. SMT contributed to formal analysis; methodology; writing—review and editing. LGS was involved in funding acquisition; project administration.

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Correspondence to Guang-shuai Li.

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Liu, Xj., Sultan, M.T. & Li, Gs. Obesity, Glycemic Traits, Lifestyle Factors, and Risk of Facial Aging: A Mendelian Randomization Study in 423,999 Participants. Aesth Plast Surg 48, 1005–1015 (2024). https://doi.org/10.1007/s00266-023-03551-4

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