Journals of Gerontology: Biological Sciences cite as: J Gerontol A Biol Sci Med Sci, 2020, Vol. 75, No. 6, 1089–1094 doi:10.1093/gerona/glz101 Advance Access publication April 25, 2019 Original Article Glutathione Serum Levels and Rate of Multimorbidity Development in Older Adults Laura M. Pérez, PhD,1,2,3,*, Babak Hooshmand, PhD,1,4 Francesca Mangialasche, PhD,1,5 Patrizia Mecocci, PhD,6 A. David Smith, PhD,7 Helga Refsum, PhD,7,8 Marco Inzitari, PhD,2,3,9 Laura Fratiglioni, PhD,1,10 Debora Rizzuto, PhD,1 and Amaia Calderón-Larrañaga, PhD1 Aging Research Center, NVS Department, Karolinska Institutet, Stockholm University, Sweden. 2Hospital Parc Sanitari Pere Virgili, 1 Barcelona, Spain. 3RE-FiT Barcelona Research Group, Vall d’Hebrón Institute of Research, Spain. 4Department of Neurology, Ulm University Hospital, Germany. 5Division of Clinical geriatrics, NVS Department, Karolinska Institutet, Stockholm, Sweden. 6Department of Medicine, Institute of Gerontology and Geriatrics, University of Perugia, Italy. 7Department of Pharmacology, University of Oxford, Oxford, UK. 8Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Norway. 9Department of Medicine, Universitat Autònoma de Barcelona, Spain. 10Stockholm Gerontology Research Center, Sweden. *Address correspondence to: Laura M. Pérez, PhD, Avinguda de Vallcarca, 169-205, 08023 Barcelona, Spain. E-mail: lperez@perevirgili.cat Received: December 1, 2018; Editorial Decision Date: April 10, 2019 Decision Editor: David Le Couteur, MBBS, FRACP, PhD Abstract We aimed to investigate the association between baseline levels of total serum glutathione (tGSH) and rate of chronic disease accumulation over time. The study population (n = 2,596) was derived from a population-based longitudinal study on ≥60-year-olds living in Stockholm. Participants were clinically assessed at baseline, 3- and 6-year follow-ups. Multimorbidity was measured as the number of chronic conditions from a previously built list of 60 diseases. Linear mixed models were applied to analyze the association between baseline tGSH levels and the rate of multimorbidity development over 6 years. We found that at baseline, participants with ≥4 diseases had lower tGSH levels than participants with no chronic conditions (3.3 vs 3.6 µmol/L; p < .001). At follow-up, baseline levels of tGSH were inversely associated with the rate of multimorbidity development (β * time: −0.044, p < .001) after adjusting for age, sex, education, levels of serum creatinine, C-reactive protein, albumin, body mass index, smoking, and time of dropout or death. In conclusion, serum levels of tGSH are inversely associated with multimorbidity development; the association exists above and beyond the link between tGSH and specific chronic conditions. Our findings support the hypothesis that tGSH is a biomarker of multisystem dysregulation that eventually leads to multimorbidity. Keywords: Biomarkers, Multimorbidity, Epidemiology, Biogerontology Recent studies have detected an association between low levels of levels of glutathione and biological aging has already been reported glutathione and specific chronic conditions such as diabetes mellitus (2,6,17,18). However, we still do not know what role, if any, gluta- (1,2), AIDS (3), cystic (4–6) and pulmonary fibrosis (6,7), chronic thione plays in the development of multimorbidity (ie, the coexist- liver injury (6,8), and some neurodegenerative conditions such as ence of multiple chronic conditions in a single individual). Parkinson disease (6,9) and Alzheimer’s disease (6,10–12). Multimorbidity is the most common clinical condition linked to Glutathione is part of the enzymatic antioxidant system and is aging; it affects more than 80% of people aged 65 years or older involved in several essential physiological processes, such as detoxi- (19). Moreover, multimorbidity often leads to several negative health fication of endogenous compounds and xenobiotics; transport and outcomes, such as functional and cognitive decline, avoidable hospi- storage of cysteine that modulates immune function (13,14); and talizations, decreased quality of life, and mortality (20,21). A faster DNA synthesis, repair, and expression (15,16). Given these multiple accumulation of chronic diseases with time has been previously biological mechanisms, it is plausible to hypothesize that glutathione suggested to be a clinical manifestation of progressive loss of re- plays a role in the aging process. Indeed, an association between low silience and homeostatic multisystem dysregulation, hallmarks of © The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America. 1089 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by- nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com 1090 Journals of Gerontology: BIOLOGICAL SCIENCES, 2020, Vol. 75, No. 6 the biological aging process (22). The rate of development and ex- Glutathione Assessment pansion of multimorbidity could thus be interpreted as proxies for Venous blood samples were taken at baseline in nonfasting con- the speed of biological aging. Few previous studies have explored ditions and stored at −80°C for all SNAC-K participants. For a interindividual heterogeneity in the rate of multimorbidity progres- random sample of 2,785 participants, these were transferred to the sion over time (23–27), and found some evidence of the detrimental University of Oxford, United Kingdom, for further analyses (30). effects of adverse socioeconomic and lifestyle conditions. So far, only Levels of tGSH (µmol/L), including both the reduced and oxidized one study looked at biological correlates of multimorbidity develop- form of glutathione, were measured through tandem mass spectros- ment prospectively, showing that higher circulating levels of inflam- copy as described previously (31). Although the concentrations of matory markers are cross-sectionally associated with multimorbidity tGSH are higher within cells than in the blood, serum tGSH levels and predict steeper rates of disease accumulation over time (28). are a good proxy of overall body glutathione (32). The coefficients The aim of our study was to investigate the association between of variation ranged between 5% and 10%. baseline levels of total serum glutathione (tGSH) and the rate of chronic disease accumulation over time in a population-based co- Covariates hort of older adults. Education was measured as the highest level of formal education and was categorized as elementary school, high school, or university or above. Serum creatinine, C-reactive protein (CRP), and albumin Methods levels were measured using routine methods at Karolinska Institutet, Study Population and Data Collection Stockholm, Sweden. CRP levels were categorized as low (≤5 mg/L), The study population was derived from the Swedish National study medium (5.1–9.9 mg/L), or high (≥10 mg/L). Body mass index (BMI) on Aging and Care in Kungsholmen (SNAC-K), a population-based was calculated by dividing weight in kilograms by the square of prospective study conducted in the Kungsholmen area of central height in meters, and participants were categorized as underweight Stockholm (29). SNAC-K is an ongoing study of a random sample (<18.5 kg/m2), of normal weight (18.5–24.9 kg/m2, reference cat- of people aged 60 years or older who live either at home or in an in- egory), overweight (25–29.9 kg/m2), or obese (≥30 kg/m2). Smoking stitution. The sample was randomly drawn from 11 age cohorts (60, was categorized as never, former, or current smoker. Dropout or 66, 72, 78, 81, 84, 87, 90, 93, 96, and ≥99 years). The two youngest death status in the 3- or 6-year follow-up was also included in the and the four oldest age groups were oversampled to make up for the analyses to account for potential selection bias. likely high attrition rate in these age groups. Of the 4,590 people invited to join the study, 3,363 participated in the baseline examin- Statistical Analysis ation (73.3% participation rate). Follow-up assessments were per- Differences in sociodemographic and clinical characteristics be- formed every 6 years for younger participants (60–78 years) and tween people with versus without information on the exposure and every 3 years for older participants (≥78 years). The current study covariates were analyzed using the chi-square test for proportions included baseline data, collected between 2001 and 2004, and data and the Mann–Whitney test for continuous variables. Comparisons from the 3- and 6-year follow-ups. of baseline characteristics according to the number of chronic dis- Data were collected at baseline and each follow-up by trained eases were performed using the chi-square test for proportions personnel in accordance with a structured protocol available at and the Jonckheere–Terpstra trend test for continuous variables. http://www.snac.org. Nurses collected demographic data and as- Differences in tGSH levels according to age group, sex, and educa- sessed participants’ physical function; psychologists administered tion level were assessed by the Jonckheere–Terpstra, Mann–Whitney, cognitive test batteries; and physicians carried out physical, geri- and Kruskal–Wallis tests, respectively. atric, and neuropsychiatric examinations. Data on past medical his- Linear mixed models were employed to analyze the longitu- tory and vital status were also available through the National Patient dinal association between baseline tGSH levels and the rate of Register and the Swedish Death Register, which have been linked to multimorbidity development over the 6-year follow-up (ie, the the SNAC-K database. number of chronic conditions in each participant in each wave). The SNAC-K was approved by the Regional Ethical Review Board in interaction term between time and tGSH level was included as a Stockholm, Sweden. Written informed consent was collected from all fixed effect; the resulting β-coefficients are interpreted as the average participants or, if the participant was not capable of providing such annual change in the number of chronic conditions across the 6-year consent, from a proxy. follow-up. Random effects were defined for the individual and time of follow-up; unstructured covariance was assumed. The exposure Multimorbidity Assessment (ie, tGSH) was operationalized both as a standardized continuous The total number of chronic conditions at baseline and follow- variable (ie, Z-scores), and categorized in quartiles. Models were ups was operationalized in accordance with a comprehensive list adjusted for age, sex, educational level, serum creatinine, CRP, and proposed by Calderón-Larrañaga and colleagues (19). This list of albumin levels, BMI category, smoking (all measured at baseline), chronic conditions is the result of a consensus reached by an inter- and time of dropout/death in a cumulative manner, in order to de- national team of geriatricians, primary care physicians, and epidemi- tect major confounders. Serum creatinine was included as a measure ologists. In brief, all codes from the International Classification of of kidney function, since alterations in protein and amino acid me- Diseases, 10th revision (ICD-10) were first classified as chronic or tabolism are present in patients with chronic kidney disease. CRP not chronic, and those classified as chronic were grouped into 60 was included as a marker of inflammation, and albumin as a proxy broader diseases categories. SNAC-K participants’ medical histories, of nutritional status, which can affect glutathione metabolism (4). laboratory test results, information on current drug use, and data BMI was included in the models because it is associated with levels from primary care and hospital medical records were used to iden- of total serum cysteine, one of the precursors of glutathione (33). tify these chronic conditions. Smoking was included given that it is related both to oxidative stress Journals of Gerontology: BIOLOGICAL SCIENCES, 2020, Vol. 75, No. 6 1091 and to disease occurrence. Additionally, three-way interactions be- Lower levels of baseline tGSH were associated with a higher tween age and sex and baseline tGSH levels * time were tested. rate of multimorbidity development and the direction and magni- tude of the association remained very stable regardless of poten- Sensitivity analyses tial confounders (Figure 2 and Supplementary Table 1). Specific First, to evaluate whether tGSH levels were associated with the pres- β-coefficients for the interaction term between follow-up time ence of specific chronic diseases instead of longitudinal changes in and 1-SD difference in the mean tGSH level were: −0.043 when multimorbidity, we repeated the analyses excluding cardiovascular, adjusting for sex, age, and education (95% CI −0.061; −0.025), neuropsychiatric, and musculoskeletal diseases from the original −0.043 when adjusting additionally for creatinine level (95% CI total count of diseases in three different models. These three groups -0.061; −0.025), −0.043 when adjusting additionally for CRP level of chronic conditions represent the most prevalent patterns of (95% CI -0.061; −0.025), −0.043 when adjusting additionally for multimorbidity, regardless of the study population and methodology albumin level and BMI (95% CI -0.061; −0.025), −0.044 when (34). Second, survival analyses were conducted using Cox propor- adjusting additionally for smoking habit (95% CI −0.062; −0.026), tional hazards regression, in order to assess the association between and −0.044 when adjusting additionally for time of dropout/death tGSH levels (ie, Z-scores) and 14-year all-cause mortality. (95% CI −0.062; −0.025). Similar findings were obtained when In all analyses, p-values < .05 were regarded as statistically sig- tGSH levels were categorized in quartiles (Figure 3). No significant nificant. Analyses were performed using Stata version 14. interactions were found with age and sex. All models provided similar results when excluding cardiovas- cular, neuropsychiatric, and musculoskeletal diseases from the ori- Results ginal total count of chronic conditions (Figure 2). Finally, lower levels The mean age of the study population at baseline was 72.8 years of baseline tGSH were associated with a higher risk of mortality, even (SD = 10.2), 61.3% were women and approximately half had in the fully adjusted model (HR 0.91, 95% CI 0.866; 0.980). When at least high school education (Table 1). The average number tGSH levels were categorized in quartiles with the highest level (4th of chronic diseases at baseline was 3.8 (SD = 2.34), and 6.46 quartile) as the reference, only the lowest level (1st quartile) was sig- (SD = 3.62) at the 6-year follow-up. Participants for whom base- nificantly associated with increased mortality (Table 2). line data on the exposure or the covariates were missing (n = 767) were older than the study population (81.6 vs 72.8 years; p < .001), more likely to be women (77.1% vs 61.3%; p < .001), took a greater number of medications (5.2 vs 3.7; p < .001), and had Discussion a greater number of chronic conditions (4.9 vs 3.8; p < .001). In this large longitudinal population-based study of older adults, No sex or education differences were found in baseline tGSH lower baseline levels of tGSH were associated with a higher rate of levels. However, mean levels of tGSH decreased significantly with multimorbidity development over 6 years. Our results were inde- increasing age (Figure 1). pendent of age, sex, educational level, serum creatinine, CRP level, Table 1. Baseline Characteristics of the Study Population by Number of Chronic Diseases Total Popu- lation 0 Disease 1 Disease 2–3 Diseases ≥4 Diseases p- n = 2,596 n = 91 n = 292 n = 966 n = 1,247 Value* Age, mean (SD) 72.8 (10.2) 64.3 (6.0) 65.2 (7.0) 69.9 (8.8) 77.3 (9.8) <.001 Age groups % (n) <.001 60–66 43.9 (1,139) 81.3 (74) 78.4 (229) 55.6 (537) 24.0 (299) 72–78 30.3 (789) 16.5 (15) 15.1 (44) 29.8 (288) 35.5 (442) 81–87 17.9 (464) 2.2 (2) 6.5 (19) 11.7 (113) 26.4 (330) ≥90 7.9 (204) 0.0 (0) 0.0 (0) 2.9 (28) 14.1 (176) Women % (n) 61.3 (1,591) 49.5 (45) 54.8 (160) 59.9 (579) 64.7 (807) .001 Education % (n) <.001 Elementary 14.7 (382) 4.4 (4) 6.9 (20) 12.7 (123) 18.9 (235) High school 49.5 (1,286) 35.2 (32) 42.3 (124) 47.4 (458) 53.8 (672) University 35.8 (928) 60.4 (55) 50.7 (148) 39.9 (385) 27.3 (340) BMI % (n) <.001 Underweight 2.5 (64) 0.0 (0) 0.7 (2) 2.1 (20) 3.4 (42) Normal weight 44.7 (1,160) 59.3 (54) 57.1 (167) 44.5 (430) 40.8 (509) Overweight 40.0 (1,039) 40.7 (37) 38.4 (112) 43.5 (420) 37.7 (470) Obese 12.8 (333) 0.00 (0) 3.8 (11) 9.9 (96) 18.1 (226) Smoking % (n) <.001 Never 45.5 (1,174) 32.9 (30) 40.5 (117) 45.2 (435) 47.7 (592) Former 39.8 (1,029) 42.9 (39) 42.9 (124) 37.2 (358) 40.9 (508) Current 14.7 (380) 24.2 (22) 16.6 (48) 17.6 (169) 11.4 (141) Number of drugs, mean (SD) 3.7 (3.3) 0.92 (1.1) 1.3 (1.7) 2.4 (2.1) 5.5 (3.4) <.001 tGSH levels in µmol/L, mean (SD) 3.6 (1.1) 3.8 (1.1) 3.9 (1.2) 3.6 (1.2) 3.5 (1.1) <.001 Notes: BMI = body mass index; SD = standard deviation; tGSH = total serum glutathione. *Chi-square test for proportions and Jonckheere–Terpstra trend test for continuous variables. 1092 Journals of Gerontology: BIOLOGICAL SCIENCES, 2020, Vol. 75, No. 6 Figure 1. Baseline mean and standard deviation of tGSH levels according to Figure 3. Estimated changes in the number of chronic conditions developed age group. tGSH = total serum glutathione. over the 6-year follow-up by baseline tGSH quartile (4th quartile as reference). tGSH = total serum glutathione. 1st tGSH quartile range: 1.31–2.75 µmol/L, 2nd tGSH quartile range: 2.75–3.44 µmol/L, 3rd tGSH quartile range: 3.44– 4.23 µmol/L, and 4th tGSH quartile range: 4.23–12.5 µmol/L. Model adjusted for age, sex, education, serum creatinine, C-reactive protein, and albumin, body mass index, smoking, and time of dropout/death. reflect an unknown underlying dysfunction that leads to a break- down of glutathione homeostasis. In both cases, tGSH levels could be used as a subclinical biomarker to predict the future develop- ment of multimorbidity and to better understand who will benefit most from interventions to reduce their future burden of disease. Previous studies have found lower glutathione levels in older adults than in young populations (2,35,36), which suggest that there is a relationship between age and glutathione levels. Higher concentrations of tGSH levels have also been reported in physically and mentally healthy long-lived older women (32), which highlights the importance of glutathione’s role in healthy aging. The associ- ation between tGSH and the presence of multimorbidity has been Figure 2. Association between baseline tGSH levels and changes in the investigated in previous studies, but only in cross-sectional studies number of chronic conditions developed over the 6-year follow-up. Results of with limited information on multimorbidity. Early in the 1990s, the main and sensitivity analyses. BMI = body mass index; CRP = C-reactive Julius and colleagues (37) measured tGSH levels in a small sample protein; CV = cardiovascular; MSK = musculoskeletal; NP = neuropsychiatric; of 33 community-dwelling adults older than 60 years. Although the tGSH = total serum glutathione. β-coefficients represent the interaction term between follow-up time and 1-SD difference in the mean tGSH level. sample was small and only 21 chronic conditions were considered, their results indicated a relationship between lower tGSH levels and a higher number of chronic diseases and worse self-rated health. and albumin, BMI, smoking, and time of dropout or death during More recently, in a study of hospital inpatients, Lang and colleagues follow-up. The association existed above and beyond the link be- (18) compared tGSH levels in 74 patients with at least one chronic tween tGSH and specific chronic conditions; thus, it seemed to be condition and a control group of 32 patients without any chronic driven by the global burden of multimorbidity. These findings sug- condition. They also observed a cross-sectional association between gest that tGSH may be a relevant biomarker of multisystem failure lower levels of tGSH and the presence of chronic conditions. that eventually leads to multimorbidity. The main strength of the current analyses was the use of data A number of hypotheses could explain our finding. On one from a longitudinal population-based study of a large sample of hand, there could be a causal relationship between low tGSH people living in the community or institutions for whom extensive levels and multimorbidity. Glutathione is one of the most im- clinical and laboratory assessments were available. Additionally, portant antioxidants in humans and plays a role in several key cell chronic conditions were identified with a clinically driven algorithm functions, including signaling and transcription processes. An im- that integrated different sources of data. The findings are precise balance in glutathione homeostasis may be due to mitochondrial and stable, as they remained unchanged despite sensitivity analyses. and DNA damage, a state of hypoxia, and/or high levels of oxida- Moreover, the association found between tGSH levels and mortality tive stress that lead to decreased glutathione synthesis or increased reinforces the significance and reliability of our findings. glutathione consumption (35). Such an imbalance could contribute One of the main limitations is the lack of tGSH measurements to cellular dysfunction and higher susceptibility to stress, leading during follow-up assessments, which may mean that we underesti- to multisystem dysregulation and faster development of chronic mated the associations because of regression dilution. Additionally, diseases. On the other hand, low levels of glutathione could also the technique used to assess serum glutathione levels allowed us to Journals of Gerontology: BIOLOGICAL SCIENCES, 2020, Vol. 75, No. 6 1093 Table 2. Association Between Baseline tGSH Levels (as Continuous and in Quartiles with 4th Quartile as Reference) and 14-Year All-Cause Mortality Fully Adjusted Model Excluding CV Diseases Excluding NP Diseases Excluding MSK Diseases HR (CI 95%) p-Value HR (CI 95%) p-Value HR (CI 95%) p-Value HR (CI 95%) p-Value tGSH (Z-score) 0.91 (0.85–0.98) .009 0.91 (0.85–0.98) .008 0.92 (0.85–0.98) .014 0.91 (0.85–0.98) .012 4th tGSH quartile Ref Ref Ref Ref Ref Ref Ref Ref 3rd tGSH quartile 1.06 (0.87–1.28) .575 1.06 (0.87–1.29) .564 1.05 (0.87–1.28) .613 1.03 (0.85–1.25) .759 2nd tGSH quartile 1.16 (0.96–1.40) .122 1.16 (0.96–1.40) .124 1.14 (0.95–1.38) .158 1.15 (0.95–1.39) .145 1st tGSH quartile 1.33 (1.10–1.60) .003 1.33 (1.11–1.61) .003 1.31 (1.09–1.58) .004 1.30 (1.07–1.56) .007 Notes: CV = cardiovascular; MSK = musculoskeletal; NP = neuropsychiatric; tGSH = total serum glutathione. Results of the main and sensitivity analyses. Models adjusted for age, sex, education, serum creatinine, C-reactive protein, and albumin serum levels, body mass index, smoking, number of chronic diseases at baseline, and time of dropout. measure tGSH, which is the sum of free and protein-bound gluta- Acknowledgments thione and glutathione disulphide levels. Thus, we could not dif- We would like to thank all the SNAC-K participants and the SNAC-K Study ferentiate between the reduced and oxidized forms of glutathione, Group for their invaluable collaboration and contributions in data collection which prevented us from ascertaining whether or not there was a and management. Authors’ Contribution: Study concept and design: L.M.P., direct relationship between the oxidative redox status and tGSH L.F., A.C.-L. Analysis and interpretation of data: L.M.P., B.H., F.M., L.F., levels. It may be argued that in people with lower tGSH levels, dis- A.C.-L. Critical revision of the manuscript for important intellectual content: eases do not accumulate but get diagnosed more rapidly due to more L.M.P., B.H., F.M., P.M., A.D.S., H.R., M.I., L.F., D.R., A.C.-L. intensive diagnostic workup in people who are already ill. However, the baseline number of chronic conditions each participant suffers from is accounted for in the linear mixed models. Our study popula- Conflict of Interest tion was younger and healthier than those excluded from the study None reported. sample, which could bias the association between tGSH levels and multimorbidity. Last, further external validation is needed to discard that our findings are artificially driven by population composition or References single-biomarker analysis (38). 1. Murakami K, Kondo T, Ohtsuka Y, Fujiwara Y, Shimada M, Kawakami Y. Impairment of glutathione metabolism in erythrocytes from patients with diabetes mellitus. Metabolism. 1989;38:753–758. Conclusion doi:10.1016/0026-0495(89)90061-9 2. 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