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The V-shaped curve relationship between fasting plasma glucose and human serum albumin in a large health checkup population in China

BMC Endocrine Disorders volume 23, Article number: 192 (2023) Cite this article

Abstract

Background

This study aimed to investigate the relationship between fasting plasma glucose (FPG) and human serum albumin (HSA) in a large health checkup population in China.

Methods

In this cross-sectional health checkup study, we enrolled a population of 284,635 subjects from Wuhu between 2011 and 2016. All participants completed the physical examination, blood biochemical examination, and blood routine examination.

Results

The prevalence of diabetes in men and women was 6.11% and 2.98%, respectively. The average level of HSA and FPG was significantly higher in men than in women (48.44 ± 3.25 vs. 47.14 ± 3.22, P < 0.0001; 5.50 ± 1.26 vs. 5.26 ± 0.94, P < 0.0001). There were significant differences in blood biochemistry and blood routine values by gender. After adjusting for confounding factors, the results showed that FPG and HSA were a V-shaped curve, and the threshold value of HSA was 40.7 mmol/L. FPG and HSA still showed a V-shaped curve after stratification by gender and age. In the male group, FPG decreased with HSA when HSA<42.3 mmol/L, and increased when HSA ≥ 42.3 mmol/L. In the female group, FPG decreased with HSA when HSA<35.7 mmol/L, and increased when HSA ≥ 35.7 mmol/L. In the age<65 group, FPG decreased with HSA when HSA<37.5 mmol/L, and increased when HSA ≥ 37.5 mmol/L. In the age ≥ 65 group, FPG decreased with HSA when HSA<43.2 mmol/L, and increased when HSA ≥ 43.2 mmol/L.

Conclusions

A V-shape relationship exists between fasting plasma glucose and human serum albumin among the Chinese health checkup population studied.

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Background

Albumin is a protein synthesized in the liver and constitutes more than half of the serum proteins. Albumin has important antioxidant activity, anti-inflammatory and anti-coagulant effects [1], and it is also a useful marker for assessing nutritional status [2]. Some previous studies have shown a positive association between higher dietary protein intake and HSA levels [3,4,5], while other newer studies do not validate that such a relationship exists. Studies have shown that it rarely changes with dietary patterns, except in the case of reduced protein consumption accompanied by adequate caloric intake [6]. A high-protein diet has been associated in some studies with an increased incidence of diabetes [7, 8]. A possible reason for such a relationship might be due to the fact that high protein intake is accompanied by stimulation of glucagon and insulin, high glycogen turnover, and increased gluconeogenesis [9]. It is reasonable to assume that there may be certain relationship between diabetes and HSA concentration. Fasting plasma glucose (FPG) concentration is the most direct indicator of diabetes, and there may be certain association between FPG and HSA concentration.

Since it is unclear whether there is some relationship between FPG and HSA concentration, studying their relationship would be helpful for early detection, diagnosis, and treatment of diabetes. Therefore, it remains crucial to further clarify their relationship. In summary, in this investigation, we conducted a cross-sectional study in a large health screening population in China to understand the relationship between FPG and HSA concentration.

Methods

Study participants

In this cross-sectional study, we enrolled a population of 432,430 subjects who had the physical examination at the Center of Health Examination, Yijishan Hospital in Wuhu city from 2011 to 2016. The inclusion criteria were: (1) subjects within the age range of 18–98 years; (2) available data on gender, age, height, weight, systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), hemoglobin (HGB), white blood cells (WBC), red blood cells (RBC), human serum albumin (HSA), and fasting plasma glucose (FPG); and (3) without a history of anti-diabetes treatment. The exclusion criteria were: (1) subjects with incomplete clinical data, and missing data; (2) subjects with a history of diabetes; and (3) subjects with morbidity history of kidney disease, gout, and cancer. All subjects underwent the physical examination, blood biochemistry, and blood tests. Diabetes mellitus was diagnosed based on FPG of ≥ 126 mg/dL (7.0 mmol/L) (fasting time 8-12 h) [10], or if the subject was taking medication to control blood glucose at the time. The study was consistent with Helsinki guidelines of the Helsinki Declaration of World Medical Association. This study was approved by the Ethics Committee of Wannan Medical College.

Questionnaire recorded

The baseline information of the study subjects included general information such as gender, age, occupation, and education. Medical history information included hypertension, diabetes mellitus, dyslipidemia or kidney disease, gout, and cancer. Whether taking antihypertensive drugs, glucose-lowering drugs, lipid-lowering drugs, etc. In addition, information on lifestyle habits such as history and frequency of smoking, history and frequency of alcohol consumption, dietary habits, and weekly exercise time were also collected and compiled [11].

Physical examination

Under the guidance of the WHO and the International Society of Hypertension [12], trained specialists used the usual methods of measuring height, weight, and blood pressure. For height measurement, the subject should stand barefoot on the floor of the height measuring machine with the body naturally extended, head straight, eyes straight ahead, upper arms naturally down, and legs straight. When measuring weight, a barefoot person should naturally stand in the middle of the weight pedal and wait for the scale number to stabilize. BMI was measured by dividing the square of height (m) by weight (kg). A mercury column sphygmomanometer was used to measure SBP and DBP. subjects were recommended to sit still for 5 min before taking their blood pressure [13].

Laboratory measurements

Fasting venous blood was obtained in the morning. Biochemical parameters (TC, TG, HDL-C, LDL-C, and FPG) were measured using a Hitachi 7600 automatic biochemistry analyzer. Routine blood tests for HGB, WBC, and RBC were performed using a Beckman Coulter Automated CBC analyzer (Beckman Coulter, Inc, Fullerton, CA, USA).

Statistical analysis

Data analyses were done using SPSS 20.0 software and R-Project 3.6.3. Mean ± standard deviation was used to express continuous variables, while frequencies and ratios were used to express discrete variables. Males and females were t-tested for demographics, physical examination, biochemistry, and blood count. The prevalence of diabetes mellitus was compared across gender using chi-square test. The relationship between FPG and HSA was validated using the generalized smooth spline method, and the node positions were automatically generated in the generalized additive model using the R software MGCV. The correlation between FPG and HSA was further investigated by linear regression of HSA nodal sites for each segment of HSA, adjusted for gender, age, BMI, SBP, DBP, TC, HGB, WBC, and RBC. Then the correlation between FPG and HSA was investigated using the generalized smoothing spline method and adjusted accordingly and linear regression was performed on the correlation with HSA. All p values were two-tailed, with a significance level of 0.05.

Results

Characteristics of subjects

Table 1 showed that a total of 284,635 subjects were included in this study that comprised 165,496 men (58.14%) and 119,139 women (41.86%), with the mean age of 48.19 ± 13.76 and 46.97 ± 13.51 years, respectively. The prevalence of diabetes in men and women was 6.11% and 2.98%, respectively. The average level of HSA and FPG was significantly higher in men than in women (48.44 ± 3.25 vs. 47.14 ± 3.22, P < 0.0001; 5.50 ± 1.26 vs. 5.26 ± 0.94, P < 0.0001). There were significant differences in blood biochemistry and blood routine values by gender.

Table 1 Comparison of demographic characteristics and biochemical indicators by genders
Full size table

Relationship between FPG and HSA

Figure 1 showed a V-shaped curve relationship between FPG and HSA, the threshold value of HSA was 40.7 mmol/L. FPG decreased with HSA when HSA<40.7 mmol/L, and increased when HSA ≥ 40.7 mmol/L, after adjustment for gender, age, BMI, SBP, DBP, TC, HGB, WBC, and RBC. These results are presented on Table 2.

Fig. 1
figure 1

Relationship between FPG and HSA. (The horizontal axis represents various levels of HSA and the vertical axis is ln odds ratio of FPG). Solid line: without adjustment; dotted line: adjustment for gender, age, BMI, SBP, DBP, TC, HGB, WBC, and RBC. Shaded area shows the 95% confidence interval

Full size image
Table 2 Association between FPG and HSA by linear regression model
Full size table

Relationship between FPG and HSA in different genders

Figure 2 showed that through gender stratification and after adjustment for age, BMI, SBP, DBP, TC, HGB, WBC, and RBC, a V-shaped curve relationship was established between FPG and HSA in the male and female groups, respectively. In the male group, FPG decreased with HSA when HSA<42.3 mmol/L, and increased when HSA ≥ 42.3 mmol/L; In the female group, FPG decreased with HSA when HSA<35.7 mmol/L, and increased when HSA ≥ 35.7 mmol/L. These data are shown on Table 3.

Fig. 2
figure 2

Relationship between FPG and HSA in men (A) and women (B) (The horizontal axis represents various levels of HSA and the vertical axis is ln odds ratio of FPG). Solid line: without adjustment; dotted line: adjustment for age, BMI, SBP, DBP, TC, HGB, WBC, and RBC. Shaded area shows the 95% confidence interval

Full size image
Table 3 Association between FPG and HSA by linear regression model in different genders
Full size table

Relationship between FPG and HSA in different ages

Figure 3 showed that through age stratification and after adjustment for gender, BMI, SBP, DBP, TC, HGB, WBC, and RBC, a V-shaped curve relationship was established between FPG and HSA in the age<65 and age ≥ 65 groups, respectively. In the age<65 group, FPG decreased with HSA when HSA<37.5 mmol/L, and increased when HSA ≥ 37.5 mmol/L; In the age ≥ 65 group, FPG decreased with HSA when HSA<43.2 mmol/L, and increased when HSA ≥ 43.2 mmol/L. These data are shown on Table 4.

Fig. 3
figure 3

Relationship between FPG and HSA in age<65 (A) and age ≥ 65 (B). (The horizontal axis represents various levels of HSA and the vertical axis is ln odds ratio of FPG). Solid line: without adjustment; dotted line: adjustment for gender, BMI, SBP, DBP, TC, HGB, WBC, and RBC. Shaded area shows the 95% confidence interval

Full size image
Table 4 Association between FPG and HSA by linear regression model in different ages
Full size table

Discussion

Given the cross-sectional nature of our study, the possible mechanisms behind our findings may only be speculated. This study found a V-shaped curve relationship between FPG and HSA, in which FPG concentrations first decreased and then increased with increasing HSA concentrations. After stratification by gender and age [14, 15], a V-shaped curve relationship between FPG and HSA was still established. The cut-off value of HSA in the male and female groups was 42.3 mmol/L and 35.7 mmol/L, respectively. And in the age<65 and age ≥ 65 groups was 37.5 mmol/L and 43.2 mmol/L, respectively.

Previous studies have shown that serum albumin is the most abundant circulating protein and its antioxidant properties are significant [16, 17]. The protective effect of anterior serum albumin concentration on impaired glycemic control may be due to its antioxidant effect, which binds to reactive oxygen species (ROS), thus preventing ROS from disrupting insulin signaling pathways and failing to induce cytotoxicity in pancreatic β-cells, thereby reducing the development of diabetes [18]. Another possible factor could be the hydration status of the participants, which is considered to be associated with higher glucose values, while dehydration is currently recognized as the only cause of high HSA [19], so it can be assumed that HSA values are higher and FPG is lower in the dehydrated state. This is consistent with the results of the first half of the V-shaped curve found in this study, and explains well the possible reasons for the decrease in FPG with increasing HSA concentrations in the early stages. The dangerous role of posterior serum albumin concentration on glycemic control may be due to the fact that higher serum albumin tends to reflect overnutrition [20], and the most direct outcome of overnutrition is obesity, which is an independent risk factor for the development of the metabolic syndrome and diabetes [21, 22]; therefore, higher serum albumin concentration may cause elevated blood glucose concentration. This is consistent with the results of the second half of the V-shaped curve found in this study, and explains well the possible reasons for the increase in FPG in the later stage as HSA concentration increases.

In summary, the first half of the V-shaped curve found in this study may be due to the fact that as the concentration of HSA gradually increases, its antioxidant capacity also becomes stronger, thus preventing ROS from disrupting the insulin signaling pathway, reducing the development of diabetes and leading to lower FPG concentration. It is also possible that this is due to the hydration status of the participants. The second half of the V-shaped curve may be due to the fact that after a certain threshold is exceeded, higher concentration of HSA tends to reflect overnutrition. Thus the incidence of metabolic syndrome and diabetes is increased, leading to higher FPG concentration.

Limitations of the study

There are several limitations to the study. First, it is a cross-sectional survey and does not address causality. However, this study used a large sample of clinical epidemiological surveys, and its findings have a high degree of objectivity. Second, we compared the baseline characteristics of the included and excluded participants in the supplementary material and found differences in the two samples. This may be due to the fact that large sample sizes improve the precision and efficacy of statistical tests, making it easier to detect differences between the two samples. Third, the study was based on information obtained from the same hospital, which would limit its representativeness. Fourth, the subjects of this thesis are health checkups, whose characteristics differ from those of community residents, which makes the results of this study representative. In the future, we will further broaden the research area and develop multidisciplinary cooperation. More studies are needed to confirm our findings and to identify the possible mechanisms involved.

Conclusions

The relationship between FPG and HSA presents a V-shaped curve among the Chinese health checkup population studied. After stratification by gender and age, in the male and female groups, in the age<65 and age ≥ 65 groups still present a V-shaped curve irrespective of adjustments for BMI, SBP, DBP, TC, HGB, WBC, and RBC.

Data Availability

The data of participants are collected by the authors and uploaded to the database, which makes it easier for the authors to use the data in the process of analyzing data and writing manuscripts. This kind of database system can conveniently shield the data irrelevant to the experiment and effectively protect the privacy of participants. The data that support the findings of this study are available from the Health Management Center at the First Affiliated Hospital of Wannan Medical College in Wuhu, China but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Health Management Center at the First Affiliated Hospital of Wannan Medical College in Wuhu, China. If someone wants to request the data from this study, they can contact Yufeng Wen (corresponding author).

Abbreviations

FPG:

Fasting plasma glucose

HSA:

Human serum albumin

BMI:

Body mass index

SBP:

Systolic blood pressure

DBP:

Diastolic blood pressure

TC:

Total cholesterol

TG:

Triglycerides

HDL-C:

High-density lipoprotein cholesterol

LDL-C:

Low-density lipoprotein cholesterol

HGB:

Hemoglobin

WBC:

White blood cells

RBC:

Red blood cells

WHO:

World Health Organization

References

  1. Zhu Y, Cai X, Liu Y, Hu M, Zhou L, Liu W, Wu J, Zhang R, Gao X, Yang W, et al. Serum albumin, but not Bilirubin, is Associated with Diabetic Chronic Vascular Complications in a chinese type 2 Diabetic Population. Sci Rep. 2019;9(1):12086.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Hwang YC, Jun JE, Hong WJ, Jin SM, Bae JC, Hur KY, Lee MK, Kim JH. Baseline level and change in serum albumin concentration and the risk of incident type 2 diabetes. J Diabetes Complications. 2018;32(1):61–6.

    Article  PubMed  Google Scholar 

  3. MacLennan WJ, Martin P, Mason BJ. Protein intake and serum albumin levels in the elderly. Gerontology. 1977;23(5):360–7.

    Article  CAS  PubMed  Google Scholar 

  4. Kelman L, Saunders SJ, Frith L, Wicht S, Corrigal A. Effects of dietary protein restriction on albumin synthesis, albumin catabolism, and the plasma aminogram. Am J Clin Nutr. 1972;25(11):1174–8.

    Article  CAS  PubMed  Google Scholar 

  5. Thalacker-Mercer AE, Campbell WW. Dietary protein intake affects albumin fractional synthesis rate in younger and older adults equally. Nutr Rev. 2008;66(2):91–5.

    Article  PubMed  Google Scholar 

  6. Levitt DG, Levitt MD. Human serum albumin homeostasis: a new look at the roles of synthesis, catabolism, renal and gastrointestinal excretion, and the clinical value of serum albumin measurements. Int J Gen Med. 2016;9:229–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wolever TM, Hamad S, Gittelsohn J, Gao J, Hanley AJ, Harris SB, Zinman B. Low dietary fiber and high protein intakes associated with newly diagnosed diabetes in a remote aboriginal community. Am J Clin Nutr. 1997;66(6):1470–4.

    Article  CAS  PubMed  Google Scholar 

  8. Wang ET, de Koning L, Kanaya AM. Higher protein intake is associated with diabetes risk in south asian Indians: the metabolic syndrome and atherosclerosis in South Asians living in America (MASALA) study. J Am Coll Nutr. 2010;29(2):130–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Linn T, Santosa B, Gronemeyer D, Aygen S, Scholz N, Busch M, Bretzel RG. Effect of long-term dietary protein intake on glucose metabolism in humans. Diabetologia. 2000;43(10):1257–65.

    Article  CAS  PubMed  Google Scholar 

  10. Petersmann A, Muller-Wieland D, Muller UA, Landgraf R, Nauck M, Freckmann G, Heinemann L, Schleicher E. Definition, classification and diagnosis of diabetes Mellitus. Exp Clin Endocrinol Diabetes. 2019;127(S 01):1–S7.

    Google Scholar 

  11. Jiang M, Zha X, Wu Z, Zhu X, Li W, Wu H, Ma J, Wang S, Wen Y. Inverted U-shaped curve relationship between red blood cell distribution width and hypertension in a large health checkup population in China. J Am Soc Hypertens. 2018;12(5):327–34.

    Article  PubMed  Google Scholar 

  12. Chalmers J, MacMahon S, Mancia G, Whitworth J, Beilin L, Hansson L, Neal B, Rodgers A, Ni Mhurchu C, Clark T. 1999 World Health Organization-International Society of Hypertension Guidelines for the management of hypertension. Guidelines sub-committee of the World Health Organization. Clin Exp Hypertens. 1999;21(5–6):1009–60.

    CAS  PubMed  Google Scholar 

  13. Zhou Y, Liu S, Wang X, Fu Y, Su F, Cao L, Zha X, Wen Y. Implications of gender-based variabilities in bone mineral density and hemoglobin levels. BMC Musculoskelet Disord. 2021;22(1):645.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Huang CC, Weng SF, Tsai KT, Chen PJ, Lin HJ, Wang JJ, Su SB, Chou W, Guo HR, Hsu CC. Long-term mortality risk after Hyperglycemic Crisis Episodes in geriatric patients with diabetes: a National Population-Based Cohort Study. Diabetes Care. 2015;38(5):746–51.

    Article  CAS  PubMed  Google Scholar 

  15. Kao Y, Hsu CC, Weng SF, Lin HJ, Wang JJ, Su SB, Huang CC, Guo HR. Subsequent mortality after hyperglycemic crisis episode in the non-elderly: a national population-based cohort study. Endocrine. 2016;51(1):72–82.

    Article  CAS  PubMed  Google Scholar 

  16. Anraku M, Chuang VT, Maruyama T, Otagiri M. Redox properties of serum albumin. Biochim Biophys Acta. 2013;1830(12):5465–72.

    Article  CAS  PubMed  Google Scholar 

  17. Belinskaia DA, Voronina PA, Shmurak VI, Jenkins RO, Goncharov NV. Serum albumin in Health and Disease: esterase, antioxidant, Transporting and Signaling Properties. Int J Mol Sci 2021, 22(19).

  18. Jun JE, Lee SE, Lee YB, Jee JH, Bae JC, Jin SM, Hur KY, Lee MK, Kim JH. Increase in serum albumin concentration is associated with prediabetes development and progression to overt diabetes independently of metabolic syndrome. PLoS ONE. 2017;12(4):e0176209.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Carroll HA, James LJ. Hydration, Arginine Vasopressin, and Glucoregulatory Health in humans: a critical perspective. Nutrients 2019, 11(6).

  20. Bernstein LH, Leukhardt-Fairfield CJ, Pleban W, Rudolph R. Usefulness of data on albumin and prealbumin concentrations in determining effectiveness of nutritional support. Clin Chem. 1989;35(2):271–4.

    Article  CAS  PubMed  Google Scholar 

  21. Piche ME, Tchernof A, Despres JP. Obesity phenotypes, diabetes, and Cardiovascular Diseases. Circ Res. 2020;126(11):1477–500.

    Article  CAS  PubMed  Google Scholar 

  22. La Sala L, Pontiroli AE. Prevention of Diabetes and Cardiovascular Disease in obesity. Int J Mol Sci 2020, 21(21).

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Acknowledgements

First of all, the authors would like to thank all the staff from the First Affiliated Hospital of Wannan Medical College who were involved in this work. Secondly, the authors would like to thank the study participants for their cooperation.

Funding

This work was supported by the Teaching Reform and Quality Improvement Plan of the Department of Education of Anhui Province-Teaching Teacher (grant number 2019jxms066); The Teaching Reform and Quality Improvement Plan of the Department of Education of Anhui Province-Teaching Team (grant number 2018jxtd153).

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Authors and Affiliations

  1. School of Public Health, Wannan Medical College, 22 West Wenchang Road, Wuhu, 241002, Anhui Province, People’s Republic of China

    Chenxu Wang, Lei Cao, Wendan Mei, Yicheng Fang, Xia Ren, Jian Hu, Fan Su, Grace Tavengana & Yufeng Wen

  2. School of Clinical Medicine, Wannan Medical College, 22 West Wenchang Road, Wuhu, 241002, Anhui Province, People’s Republic of China

    Mingfei Jiang

  3. School of Laboratory Medicine, Wannan Medical College, 22 West Wenchang Road, Wuhu, 241002, Anhui Province, People’s Republic of China

    Huan Wu

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  1. Chenxu Wang
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Contributions

YF Wen designed the research. CX Wang, L Cao, WD Mei, YC Fang, X Ren participated in data collection and analysis and drafted the manuscript. J Hu, F Su, Grace Tavengana, MF Jiang, H Wu helped analyze data and manuscript development. YF Wen provided research funding and software support. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yufeng Wen.

Ethics declarations

Ethics approval and consent to participate

Verbal informed consent was obtained from each subject prior to the conduct of the study. All protocols are carried out in accordance with relevant guidelines and regulations. Ethical approval was received from the Medical Ethics Committee of Wannan Medical College. This study was based on data from Health Management Center at the First Affiliated Hospital of Wannan Medical College in Wuhu, China. Since the participants in this study are people who came to the hospital for routine physical examinations, the Medical Ethics Committee of Wannan Medical College approved that the study only needs to obtain the participant’s verbal informed consent. Therefore, this study did not have the participants’ written informed consent.

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Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Wang, C., Cao, L., Mei, W. et al. The V-shaped curve relationship between fasting plasma glucose and human serum albumin in a large health checkup population in China. BMC Endocr Disord 23, 192 (2023). https://doi.org/10.1186/s12902-023-01441-z

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Keywords

BMC Endocrine Disorders

ISSN: 1472-6823

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