Short leukocyte telomere length and high plasma phospholipid fatty acids increase the risk of type 2 diabetes

in Endocrine Connections
Authors:
Chan Yang School of Nursing, Ningxia Medical University, Yinchuan, Ningxia, China
School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, China

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Yadi Zhang School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, China

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Juan Li School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, China

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Xiaowei Liu School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, China

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Jiangwei Qiu School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, China

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Jiaxing Zhang School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, China

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Xiuying Liu School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, China
Key Laboratory of Environmental Factors and Chronic Disease Control, Ningxia Medical University, Yinchuan, Ningxia, China

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Yuhong Zhang School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, China
Key Laboratory of Environmental Factors and Chronic Disease Control, Ningxia Medical University, Yinchuan, Ningxia, China

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Yi Zhao School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, China
Key Laboratory of Environmental Factors and Chronic Disease Control, Ningxia Medical University, Yinchuan, Ningxia, China

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Correspondence should be addressed to Y Zhang or Y Zhao: zhangyh@nxmu.edu.cn or 20040017@nxmu.edu.cn

*(C Yang and Y Zhang contributed equally to this work)

DOI:
https://doi.org/10.1530/EC-24-0033
Volume/Issue:
Volume 13: Issue 9
Article ID:
e240033
Article Type:
Research Article
Online Publication Date:
13 Oct 2024
Copyright:
© the author(s) 2024
Collections:
Metabolic Syndrome and Diabetes
Open access

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In the last 40 years, there has been a notable rise in the occurrence of diabetes within China, leading to the country now having the highest number of individuals affected by this condition globally. This prospective observational study examined the effect of different baseline relative leukocyte telomere length (RTL) and the combined effect of baseline RTL and plasma phospholipid fatty acid (PPFA) on the risk of developing diabetes. Adults from Ningxia Province who underwent baseline and follow-up surveys were included in the study. The correlation between the baseline RTL and PPFA was investigated using a multiple linear regression model. The combined effects of baseline RTL and PPFA levels on the risk of developing type 2 diabetes mellitus (T2DM) were investigated using a Cox regression model with time as the covariate. A total of 1461 study subjects were included in this study. According to the diagnostic criteria of the Chinese Diabetes Society, 141 subjects developed T2DM during the follow-up period. The baseline age was negatively correlated with RTL. After adjustment for age, C16:0, C18:1 n-9, C20:4 n-6, C20:3 n-3, and monounsaturated fatty acid (MUFA) concentrations were negatively correlated with RTL. Multiple linear regression analysis showed that C16:0 and MUFA concentrations influenced RTL. Subjects with shorter RTL at baseline had a higher risk of developing diabetes than those with longer RTL. Subjects with shorter RTL and higher C16:0 and MUFA concentrations at baseline had a higher risk of developing T2DM than those with longer RTL and lower C16:0 and MUFA concentrations. Our findings indicated that PPFA affects changes in RTL. In addition, RTL and PPFA are associated with the occurrence of T2DM.

Abstract

In the last 40 years, there has been a notable rise in the occurrence of diabetes within China, leading to the country now having the highest number of individuals affected by this condition globally. This prospective observational study examined the effect of different baseline relative leukocyte telomere length (RTL) and the combined effect of baseline RTL and plasma phospholipid fatty acid (PPFA) on the risk of developing diabetes. Adults from Ningxia Province who underwent baseline and follow-up surveys were included in the study. The correlation between the baseline RTL and PPFA was investigated using a multiple linear regression model. The combined effects of baseline RTL and PPFA levels on the risk of developing type 2 diabetes mellitus (T2DM) were investigated using a Cox regression model with time as the covariate. A total of 1461 study subjects were included in this study. According to the diagnostic criteria of the Chinese Diabetes Society, 141 subjects developed T2DM during the follow-up period. The baseline age was negatively correlated with RTL. After adjustment for age, C16:0, C18:1 n-9, C20:4 n-6, C20:3 n-3, and monounsaturated fatty acid (MUFA) concentrations were negatively correlated with RTL. Multiple linear regression analysis showed that C16:0 and MUFA concentrations influenced RTL. Subjects with shorter RTL at baseline had a higher risk of developing diabetes than those with longer RTL. Subjects with shorter RTL and higher C16:0 and MUFA concentrations at baseline had a higher risk of developing T2DM than those with longer RTL and lower C16:0 and MUFA concentrations. Our findings indicated that PPFA affects changes in RTL. In addition, RTL and PPFA are associated with the occurrence of T2DM.

Keywords: type 2 diabetes mellitus; plasma phospholipid fatty acids; leukocyte telomere length; prospective studies

Introduction

In recent years, the incidence and prevalence of diabetes have been increasing worldwide, and this is attributed to socioeconomic development, lifestyle changes, and a growing population of older people (1). According to the International Diabetes Federation (2), the global crude prevalence of diabetes among adults aged 20–79 years in 2021 was 10.5%, with approximately 537 million adults living with diabetes. Over the past four decades, the prevalence of diabetes in China has increased by approximately 19-fold (2, 3). China now has the largest diabetic population worldwide, and the prevalence of prediabetes has been increasing over recent years (4). The condition of prediabetes occurs before the development of type 2 diabetes mellitus (T2DM), which is a high-risk state for diabetes. Identifying risk factors of incident diabetes is therefore crucial for diabetes prevention.

The relationship between dietary fatty acid (FA) intake and diabetes has received extensive attention (5), with elevated plasma FA linked to insulin resistance and T2DM (6, 7). FAs are a major component of lipids, and their chain length, degrees of saturation, and spatial configurations confer different properties and physiological functions to lipids (8). Generally, lipids support growth and overall well-being. The major sources of FAs in the human body include dietary intake and endogenous FA metabolism and synthesis. Since the FA composition in tissues reflects nutritional and metabolic status and is not affected by recall bias, the FA level in tissues (plasma or red blood cells) is often used as a biomarker of recent dietary fat intake in population-based epidemiological studies (9, 10).

Various chronic diseases such as diabetes, cardiovascular disease, neurodegenerative disease, and cancer are associated with aging (11). Telomere is considered as a significant mediator for aging and age-related disease. The major physiological function of telomeres is to protect the integrity of the genome and chromosomes of eukaryotes (12, 13). Throughout the human lifespan, telomeres in normal human somatic cells undergo gradual shortening with each cycle of cell division (14). Genes, environment, and lifestyle all influence telomere shortening (15). Telomere length is usually measured as leukocyte telomere length (LTL) since it is relatively easy to extract leukocytes from human blood. Studies have shown that short and dysfunctional telomeres are linked to diabetes (16). However, most studies have focused on the association between telomeres and diabetes, and these studies have focused only on a single transversal profile and relationship.

Indeed, the contemporary relationship of telomere length with environmental exposures and lifestyles has made it an attractive subject in the epidemiological research on age-related chronic diseases (17). Our preliminary data indicate that (18) plasma phospholipid fatty acids (PPFAs) are closely associated with relative leukocyte telomere length (RTL) in the population living in rural areas of Ningxia Province. The prospective association of telomere shortening and circulating FAs with diabetes risk remains largely undetermined. The goal of this study was to evaluate the prospective association between the combined action of baseline RTL, PPFA, and incident T2DM after 9.46 years of follow-up in a longitudinal study of a northwest China natural population cohort.

Methods

Study design and participants

The study participants were selected from the Northwestern (Ningxia Province) natural population cohort, established by the ‘Precision Medicine Research’ Special Project under the Key Research and Development Program of the Ministry of Science and Technology. Baseline, and follow-up surveys of this cohort were performed in two towns of Wuzhong City between 2008 and 2020 and in two towns of Shizuishan City between 2012 and 2019 in Ningxia Province.

The inclusion criteria were as follows: (i) residents aged >18 years who participated in the baseline survey between 2008 and 2012 and (ii) residents who participated in the follow-up survey between 2019 and 2020. The exclusion criteria were as follows: (i) a fasting plasma glucose level of ≥7.0 mmol/L during the baseline survey; (ii) presence of severe infectious disease, mental illness, tumors, or other severe diseases detected during the baseline survey; (iii) inability to reach the survey site due to illness or disability, long-term work away from home, or hospitalization during the follow-up survey; (iv) missing data in the physical examination and biochemical marker blood test results or missing questionnaire responses from the baseline and follow-up surveys; and (v) death during the follow-up. In the end, 1461 individuals, with a mean follow-up period of 9.46 years, were included in this study (Fig. 1).

Figure 1
Figure 1

Flowchart for participants recruited.

Citation: Endocrine Connections 13, 9; 10.1530/EC-24-0033

This study was approved by the Ethics Committee of Ningxia Medical University. All the study participants consented to participate in the study in writing.

Questionnaire survey and body measurements

The questionnaire included questions on general demographic characteristics, lifestyle behavior (such as tobacco use, alcohol consumption, tea consumption, and physical activity level), and medical history. The baseline survey was conducted indoors in a warm room where the subjects were instructed to remove their shoes, hats, and heavy coats. Subject height was measured using a metric scale (the smallest scale division was 5 mm), and weight was measured using a weighing scale (accurate to 0.1 kg). Waist and hip circumference were measured using a measuring tape (the smallest scale division was 1 mm), and blood pressure was measured using a digital sphygmomanometer (accurate to 1 mm Hg), with the subject resting in the sitting position for 5–10 min. The above measurements were repeated twice, and the mean value was used in the final analyses.

Collection of blood specimens and laboratory testing

Peripheral venous blood samples were collected from the study subjects in the early morning after 8-h fasting. The venous blood sampling was performed by professional nurses in strict compliance with standard operating procedures. The blood samples were well-labeled, and information regarding the collection time and subject number was captured. Afterward, the specimens were quickly centrifuged and sub-packaged, maintained at a low temperature, and then transported to a refrigerator for storage at −80°C.

During the baseline survey, fasting plasma glucose levels were measured using a rapid plasma glucose meter (LifeScan, Milpitas, CA, USA). Fasting serum insulin levels at baseline were determined using the chemiluminescent enzyme-linked immunosorbent assay. Total cholesterol (TC), triglyceride (TG), and high-density lipoprotein cholesterol (HDL-C) levels were determined using enzymatic assays (CHOD-PAP, Roche Diagnostics GmbH). The low-density lipoprotein cholesterol (LDL-C) level was calculated using the Friedewald formula (19). The homeostasis model assessment of insulin resistance index (HOMA-IR) (20), quantitative insulin sensitivity check index (QUICKI) (21), and homeostasis model assessment of β-cell function index (HOMA-β) (20) were calculated using the fasting plasma glucose and insulin concentrations to evaluate insulin resistance and β-cell functions.

After the PPFAs were subjected to methyl esterification via the direct method, the plasma concentration of PPFAs was quantified and determined using gas chromatography (Agilent Technologies, 7890 N) equipped with capillary columns (Agilent J&W DB-23). C17:0 was the internal standard.

DNA was extracted from the whole blood of the study subjects using the blood DNA extraction kit (D3392, OMEGA, Guangzhou, China). Real-time PCR (RT-PCR) was performed using the designated kit for RT-PCR (RR820, Takara) according to the manufacturer’s instructions. The 36B4 was used as the internal reference gene. The RT-PCR program included a holding stage of 95°C for 30 s, 40 cycles of 95°C for 5 s, and 60°C for 30 s. This was followed by a melt curve stage at 95°C for 10 s, 65°C for 5 s, and 95°C for 5 s. Each sample was tested in duplicate. The fluorescence intensity of TB Green in the reaction mixture was detected, and the RTL in peripheral blood leukocytes was calculated based on the △Ct method using the T/S = (2Ct(telomere) / 2Ct(36B4))−1=2−△Ct formula.

Diagnostic criteria and definitions of the relevant study variables

The diagnostic criteria for type 2 diabetes based on the Guideline of the Prevention and Treatment of T2DM in China (2020 Edition) are as follows (22): fasting plasma glucose level ≥7.0 mmol/L, typical symptoms of T2DM (polydipsia, polyuria, polyphagia, and weight loss due to unknown causes), or a past medical history of T2DM. Prediabetes was defined as a fasting plasma glucose ≥6.1 mmol/L and <7.0 mmol/L. Normoglycemia was defined as a fasting plasma glucose level <6.1 mmol/L. Body mass index (BMI) was calculated using the following formula: BMI (kg/m2) = weight (kg)/height (m2). Smokers smoked daily before the survey and reported to have smoked ≥100 cumulative cigarettes during their lifetime. Moreover, a subset of subjects reported drinking alcohol in the previous year, with at least one drinking episode per week. They also reported drinking tea every week in the previous year. Regarding physical exercise, the study subjects reported exercising for >30 min at least three times a week in their spare time or exercising for >150 min weekly. A positive family history of diabetes, hypertension, and coronary heart disease was reported in the first-degree relatives (parents) of the study subjects. The subjects had hypertension or coronary heart disease.

Data organization and analysis

All statistical analyses were performed using Stata version 16.0 (StataCorp LLC). A descriptive statistical analysis of the general survey data was performed. Normally distributed measurement data were expressed as mean ± s.d. Non-normally distributed data were represented using the median and interquartile range. Moreover, enumeration data were presented as numbers and percentages. Differences between multiple groups of normally distributed data were analyzed using the analysis of variance. Non-normally distributed data were analyzed using nonparametric tests. Differences between multiple groups were analyzed using the chi-square test. After non-normally distributed measurement data were subjected to logarithmic transformations for data normalization, the correlation between the log-transformed data and RTL was evaluated using the Pearson correlation analysis. In addition, the correlation between the age-adjusted indicators was analyzed using partial correlation analysis. The correlation between the PPFA levels and RTL after adjustment for confounding factors, including age, sex, biochemical indicators, and lifestyle choices, was analyzed using a multiple linear regression model (criteria for inclusion of variables: α = 0.05, criteria for elimination of variables: 0.1), with the log-transformed RTL as the dependent variable and the log-transformed FA level as the independent variable. The interaction terms between the follow-up time and covariates were constructed, and the correlation between the baseline RTL and phospholipid FA levels and the risk of developing T2DM was analyzed using time-dependent covariates of Cox regression models. Cox regression models were used to examine the effects of covariates on time-to-events. The statistical significance was set at a two-tailed P < 0.05.

Results

General data of the study subjects at baseline

This prospective observational study involved the Northwestern (Ningxia Province) natural population cohort that participated in the first-phase surveys. Following the exclusion of 93 patients with T2DM from the baseline survey cohort, 1978 study subjects were followed up, and the follow-up was completed in 1461 subjects. Some study participants were lost during follow-up. No significant difference was observed between the baseline data of the subjects who completed follow-up and those who did not.

A total of 598 male and 863 female subjects were included in this study, and the mean baseline age of the participants was 47.53 ± 10.53 years (Table 1). Significant differences were observed in the baseline age, weight, BMI, hip circumference, diastolic blood pressure, TC, HDL-C, HOMA-IR, QUICKI, educational level, marital status, lifestyle choices, medical history, and family history of hypertension between the male and female subjects (P < 0.05 for all the variables). As shown in Table 2, significant differences were also observed in the baseline levels of C18:3 n-3 and n-3 polyunsaturated FAs (PUFAs) between male and female subjects (P < 0.05 for all the variables). As shown in Supplementary Table 2 (see section on supplementary materials given at the end of this article), significant differences were also observed in the baseline levels of C16:0, C18:0, C18:1 n-9, C18:2 n-6, C20:4 n-6, C18:3 n-3, C22:6 n-3, saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), n-6 PUFAs, n-3 PUFAs, and PUFAs between the prediabetes group and normal group (P < 0.05 for all the variables).

Table 1

Baseline characteristics of participants.
Characteristics Male (n = 598) Female (n = 863) P
X̅ ± S
 Age (years) 48.92 ± 10.70 46.57 ± 10.30 <0.001
 Weight (kg) 65.46 ± 9.36 58.26 ± 8.77 <0.001
 BMI (kg/m2) 23.24 ± 2.98 23.79 ± 3.34 0.001
 WC (cm) 81.90 ± 9.28 79.51 ± 9.35 0.139
 HC (cm) 91.97 ± 5.94 92.63 ± 6.12 <0.05
 SBP (mm Hg) 124.01 ± 17.83 123.57 ± 19.80 0.661
 DBP (mm Hg) 79.91 ± 11.38 78.63 ± 11.24 0.034
 FBG (mmol/L) 5.57 ± 0.82 5.63 ± 0.63 0.101
 FINS (µIU/mL) 5.94 ± 4.20 6.33 ± 3.97 0.072
 TC (mmol/L) 3.87 ± 0.78 3.95 ± 0.86 0.044
 TG (mmol/L) 1.33 ± 0.87 1.27 ± 0.81 0.166
 HDL-C (mmol/L) 1.26 ± 0.31 1.33 ± 0.35 <0.001
 LDL-C (mmol/L) 2.00 ± 0.64 2.05 ± 0.71 0.171
 HOMA-β 61.24 ± 44.93 62.73 ± 39.99 0.515
 HOMA-IR 1.48 ± 1.12 1.60 ± 1.10 0.045
 QUICKI 0.71 ± 0.14 0.68 ± 0.11 <0.001
n (%)
Education levels <0.001
 Illiteracy 180 (30.1) 421 (48.8)
 Elementary school 188 (31.4) 243 (28.2)
 Middle school and above 230 (38.5) 199 (23.1)
Marital status 0.007
 Married 587 (98.2) 825 (95.6)
 Others 11 (1.8) 38 (4.4)
Smoking status <0.001
 Yes 242 (40.5) 7 (0.8)
Alcohol drinking status <0.001
 Yes 127 (21.2) 8 (0.9)
Tea drinking status <0.001
 Yes 357 (59.7) 391 (45.3)
Physical exercise <0.001
 Yes 59 (9.9) 36 (4.2)
Family history of diabetes 0.566
 Yes 9 (1.5) 10 (1.2)
Family history of hypertension 0.027
 Yes 61 (10.2) 60 (7.0)
Family history of coronary heart disease 0.381
 Yes 7 (1.2) 15 (1.7)
Previous hypertension 0.012
 Yes 57 (9.5) 120 (13.9)
Previous coronary heart disease 0.066
 Yes 22 (3.7) 50 (5.8)

Values are presented as the mean ± s.d.or number (%). P-values were obtained using the ANOVA or chi-squared test.

BMI, body mass index; DBP, diastolic blood pressure; FPG, fasting blood glucose; FINS, fasting insulin; HC, hip circumference; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostasis model assessment of insulin resistance; HOMA-β, homeostasis model assessment of β-cell function; LDL-C, low-density lipoprotein cholesterol; QUICKI, quantitative insulin sensitivity check index; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; WC, waist circumference.

Table 2

Plasma phospholipid fatty acids of study participants at baseline (M(QL-QU)) (ng/mL).
Variable Male Female P
RTL 0.88 (0.38–2.73) 0.97 (0.32–2.77) 0.626
C16:0 44.03 (29.47–64.81) 46.65 (30.96–72.09) 0.220
C18:0 39.64 (27.11–56.77) 41.38 (27.55–59.42) 0.656
SFAs 85.46 (57.20–125.49) 88.21 (59.77–128.95) 0.318
C18:1 n-9 1.90 (1.68–2.05) 1.92 (1.68–2.04) 0.854
MUFAs 85.56 (59.49–118.35) 87.45 (60.29–115.23) 0.813
C18:2 n-6 53.93 (35.93–76.15) 57.39 (39.14–78.26) 0.095
C20:4 n-6 20.65 (12.89–30.14) 21.55 (13.91–78.26) 0.696
n-6 PUFAs 73.58 (52.10–107.33) 78.74 (56.90–105.99) 0.165
C18:3 n-3 4.23 (2.27–7.47) 5.06 (2.87–8.82) 0.006
C20:3 n-3 2.91 (1.66–5.71) 2.57 (1.48–5.41) 0.438
C22:6 n-3 5.63 (3.84–8.15) 5.38 (3.85–8.04) 0.920
n-3 PUFAs 12.24 (6.68–19.40) 13.42 (8.20–22.38) 0.014
PUFAs 87.12 (61.81–119.67) 93.57 (66.36–125.99) 0.089
n-6/n-3 5.96 (4.05–9.98) 5.56 (3.76–8.79) 0.111

Values are presented as (M(QL-QU)). P-values were obtained using the nonparametric test.

MUFAs, monounsaturated fatty acids; PUFAs, polyunsaturated fatty acids; RTL, relative telomere length; SFAs, saturated fatty acids.

Correlation analysis of the subject baseline data

Based on the relevant diagnostic criteria, 1190 (81.5%) subjects had baseline plasma glucose levels within the normal range, and 271 (18.5%) subjects were in the prediabetes group (Supplementary Table 1). Compared with the normal group, the percentage of participants with hypertension and coronary heart disease was higher in the prediabetes group (Supplementary Table 1). As shown in Table 3, the baseline age of the study subjects was negatively correlated with the baseline RTL (r = −0.098, P < 0.05), and the age of the subjects with normoglycemia was negatively correlated with RTL (r = −0.108, P< 0.05). The data were adjusted for age in subsequent correlation analyses. After adjustment for age, no correlation was observed between the body measurements and RTL.

Table 3

Correlation analysis of relative leukocyte telomere length with age, metabolic, and anthropometric variables.
Variable Before adjustment After adjustment
Normal Prediabetes Total Normal Prediabetes Total
r P r P r P r P r P r P
Age (years) −0.108 0.004 −0.006 0.938 −0.098 0.003 – – – – – –
Weight (kg) 0.047 0.207 −0.033 0.669 0.026 0.442 0.054 0.148 −0.034 0.662 0.030 0.370
BMI (kg/m2) 0.022 0.557 0.027 0.732 0.012 0.713 0.036 0.339 0.027 0.733 0.024 0.467
WC (cm) −0.040 0.290 0.006 0.940 −0.039 0.244 0.001 0.984 0.007 0.926 −0.005 0.888
HC (cm) −0.020 0.601 −0.098 0.204 −0.043 0.200 0.001 0.989 −0.098 0.205 −0.027 0.425
SBP (mm Hg) −0.043 0.249 −0.031 0.690 −0.049 0.146 −0.007 0.842 −0.031 0.691 −0.016 0.645
DBP (mm Hg) 0.033 0.376 −0.043 0.580 0.016 0.631 0.054 0.145 −0.043 0.584 0.036 0.281
FBG (mmol/L) −0.004 0.904 −0.024 0.757 −0.050 0.133 −0.002 0.963 −0.024 0.762 −0.039 0.242
FINS 0.143 <0.001 0.156 0.043 0.142 <0.001 0.129 0.001 0.156 0.043 0.133 <0.001
HOMA-β 0.143 <0.001 0.175 0.023 0.161 <0.001 0.126 0.001 0.175 0.024 0.146 <0.001
HOMA-IR 0.144 <0.001 0.137 0.076 0.130 <0.001 0.127 0.001 0.137 0.076 0.120 <0.001
QUICKI −0.146 <0.001 −0.174 0.024 −0.139 <0.001 −0.128 0.001 −0.174 0.024 −0.127 <0.001
TC (mmol/L) −0.070 0.059 0.054 0.483 −0.054 0.111 −0.041 0.272 0.059 0.446 −0.025 0.466
TG (mmol/L) 0.015 0.686 0.037 0.629 0.007 0.834 0.033 0.384 0.038 0.627 0.022 0.520
HDL-C (mmol/L) −0.115 0.002 −0.043 0.581 −0.103 0.002 −0.099 0.008 −0.043 0.582 −0.087 0.010
LDL-C (mmol/L) −0.038 0.309 0.051 0.512 −0.022 0.509 −0.017 0.657 0.053 0.491 −0.002 0.963

Relative leukocyte telomere lengths were log transformed. Data are Pearson's correlation coefficients (r) with corresponding P-values in parentheses.

BMI, body mass index; DBP, diastolic blood pressure; FPG, fasting blood glucose; FINS, fasting insulin; HC, hip circumference; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostasis model assessment of insulin resistance; HOMA-β, homeostasis model assessment of β-cell function; LDL-C, low-density lipoprotein cholesterol; QUICKI, quantitative insulin sensitivity check index; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; WC, waist circumference.

As shown in Table 3, FINS, HOMA-β, and HOMA-IR of the study subjects were positively correlated with RTL after adjustment for age (P < 0.05 for all the variables). In addition, QUICKI and HDL-C levels were negatively correlated with RTL ( P < 0.05 for all the variables). As shown in Table 4, C16:0, C18:1 n-9, C20:4 n-6, C20:3 n-3, and MUFA levels in the study subjects were negatively correlated with RTL (P < 0.05 for all the variables). The C18:1 n-9 level in normoglycemic subjects was negatively correlated with RTL (P < 0.05 for all the variables). Furthermore, C20:4 n-6 and C22:6 n-3 levels in prediabetic subjects were negatively correlated with RTL (P < 0.05 for all the variables) after adjustment for age.

Table 4

Correlation analysis of relative leukocyte telomere length with phospholipid fatty acid levels.
Variable Before adjustment After adjustment
Normal Prediabetes Total Normal Prediabetes Total
r P r P r P r P r P r P
C16:0 −0.109 0.008 −0.164 0.045 −0.128 <0.001 −0.131 0.056 −0.077 0.529 −0.126 0.034
C18:0 −0.065 0.118 −0.196 0.016 −0.099 0.007 −0.091 0.183 −0.075 0.539 −0.091 0.126
SFAs −0.094 0.022 −0.188 0.021 −0.122 0.001 −0.112 0.104 −0.097 0.422 −0.115 0.052
C18:1 n-9 −0.152 <0.001 −0.225 0.006 −0.172 <0.001 −0.175 0.010 −0.131 0.280 −0.162 0.006
MUFAs −0.122 0.003 −0.270 0.001 −0.160 <0.001 −0.104 0.129 −0.192 0.111 −0.128 0.031
C18:2 n-6 −0.089 0.031 −0.125 0.129 −0.104 0.005 −0.057 0.404 −0.105 0.386 −0.071 0.230
C20:4 n-6 −0.080 0.054 −0.266 0.001 −0.121 0.001 −0.078 0.253 −0.327 0.006 −0.137 0.020
n-6 PUFAs −0.092 0.025 −0.167 0.041 −0.114 0.002 −0.067 0.330 −0.182 0.131 −0.099 0.095
C18:3 n-3 −0.100 0.020 −0.167 0.046 −0.123 0.001 0.007 0.915 −0.069 0.568 −0.019 0.748
C20:3 n-3 −0.048 0.334 −0.121 0.226 −0.058 0.188 −0.124 0.070 −0.133 0.274 −0.118 0.047
C22:6 n-3 −0.012 0.816 −0.175 0.068 −0.057 0.213 −0.060 0.382 −0.271 0.023 −0.109 0.066
n-3 PUFAs −0.062 0.133 −0.190 0.021 −0.095 0.010 −0.053 0.438 −0.171 0.158 −0.081 0.174
PUFAs −0.086 0.037 −0.171 0.037 −0.111 0.003 −0.072 0.298 −0.189 0.117 −0.104 0.080
n-6/n-3 −0.020 0.638 0.096 0.247 0.005 0.896 −0.002 0.982 −0.026 0.833 −0.006 0.920

Relative leukocyte telomere length and plasma phospholipid fatty acids in each group were log transformed. Data are Pearson's correlation coefficients (r) with corresponding P-values in parentheses.

MUFAs, monounsaturated fatty acids; PUFAs, polyunsaturated fatty acids; SFAs, saturated fatty acids.

Multiple linear regression analysis of subject baseline data

Multiple linear regression analysis was performed with the log-transformed RTL as the dependent variable and the log-transformed levels of the PPFA components and subclasses (with a significant correlation with RTL) as the independent variables. The tolerance of all the independent variables was >0.2, and the VIF score was <10, indicating no multicollinearity. As shown in Table 5, regression analysis suggests that C16:0 (β = −0.63, P < 0.05) and MUFA (β = −2.03, P < 0.05) levels negatively influenced RTL after adjustment for age, general data, lifestyle habits, medical and family history, body measurements, and biochemical indicators. Regression analysis showed that C16:0 (β = −0.52, P < 0.05) level negatively influenced RTL in normoglycemic subjects after adjustment for relevant variables (Supplementary Table 3). Furthermore, MUFA (β = −2.03, P < 0.05) level influenced RTL in prediabetic subjects (Supplementary Table 4).

Table 5

Multiple linear regression analysis of baseline relative leukocyte telomere length and phospholipid fatty acids.
Variables Model 1 Model 2 Model 3 Model 4
β (95% CI) P β (95% CI) P β (95% CI) P β (95% CI) P
C16:0 −0.45 (−0.75, −0.15) 0.004 −0.48 (−0.79, −0.17) 0.002 −0.54 (−0.85, −0.22) 0.001 −0.63 (−1.16, −0.09) 0.022
C18:1 n-9 −0.29 (−1.34, 0.77) 0.595 −0.30 (−1.37, 0.78) 0.591 −0.23 (−1.33, 0.87) 0.679 −2.22 (−4.47, 0.04) 0.054
C20:4 n-6 0.05 (−0.23, 0.34) 0.708 0.07 (−0.22, 0.36) 0.641 0.07 (−0.22, 0.36) 0.618 0.25 (−0.31, 0.81) 0.386
C20:3 n-3 −0.07 (−0.26, 0.11) 0.444 −0.07 (−0.26, 0.12) 0.475 −0.08 (−0.27, 0.11) 0.418 0.05 (−0.33, 0.43) 0.799
SFAs 0.27 (−0.42, 0.96) 0.445 0.23 (−0.49, 0.94) 0.528 0.07 (−0.68, 0.83) 0.851 0.04 (−1.78, 1.86) 0.963
MUFAs −0.81 (−1.53, −0.08) 0.030 −0.85 (−1.60, −0.10) 0.027 −0.92 (−1.71, −0.14) 0.022 −2.03 (−3.96, −0.10) 0.040
n-6 PUFAs 0.06 (−0.67, 0.79) 0.876 0.15 (−0.62, 0.92) 0.698 0.45 (−0.39, 1.29) 0.286 0.41 (−1.23, 2.06) 0.606
n-3 PUFAs −0.12 (−0.60, 0.35) 0.606 −0.14 (−0.62, 0.35) 0.581 −0.21 (−0.71, 0.29) 0.416 0.22 (−0.91, 1.36) 0.687

Model 1, adjusted for age; model 2, adjusted for age, sex, general conditions, lifestyle; model 3, adjusted for model 2 plus medical history and family history, anthropometric variables; model 4, adjusted for model 3 plus metabolic variables.

MUFAs, monounsaturated fatty acids; PUFAs, polyunsaturated fatty acids; SFAs, saturated fatty acids.

Baseline RTL, C16:0, MUFA levels, and the risk of developing T2DM

After the study, participants were divided into tertiles based on baseline RTL (tertile 1: short RTL group (T1); tertile 2: medium RTL group (T2); tertile 3: long RTL group (T3)), and the correlation between different baseline telomere lengths and the risk of developing T2DM was analyzed using time-dependent covariates of Cox regression models, with the incidence of T2DM as the dependent variable. As shown in Fig. 2, the T1 group (hazard ratio (HR), 1.28; 95% CI, 1.08–1.52; P = 0.004) and the T2 group (HR, 1.25; 95% CI, 1.06–1.48; P = 0.009) in the baseline population were at a greater risk of developing T2DM than the T3 group with the longest baseline RTL (no adjustment in model 1). After models 2–4 were adjusted for age, sex, general data, lifestyle habits, body measurements, and biochemical indicators, the T1 and T2 groups were at a greater risk of developing T2DM than the T3 group. Also, among the normoglycemic and prediabetic subjects at baseline, those with shorter telomeres were at a higher risk of developing T2DM than those with longer telomeres (Fig. 2).

Figure 2
Figure 2

Risks for incidence of diabetes according to relative leukocyte telomere length at 9.46-year follow-up. (A) Total participants. (B) Participants with normal blood glucose. (C) Participants with prediabetes. Data are HR (95% CIs) based on Cox regression models. Model 1: no adjustment; model 2: adjusted for age and sex; model 3: adjusted for model 2 plus general conditions, lifestyle; model 4: adjusted for model 3 plus medical history, family history, anthropometric and metabolic variables; model 5: adjusted for model 4 plus phospholipid fatty acids.

Citation: Endocrine Connections 13, 9; 10.1530/EC-24-0033

The study subjects were divided into tertiles of baseline C16:0 and MUFA levels (tertile 1, low-level group (C1, M1); tertile 2, medium-level group (C2, M2); tertile 3, high-level group (C3, M3)). These three groups were arranged in pairs with the T1, T2, and T3 groups to create nine combinations of circumstances (T1–C1, T1–C2, T1–C3, T2–C1, T2–C2, T2–C3, T3–C1, T3–C2, and T3–C3 groups). The risk of developing T2DM was compared between the nine combinations of circumstances using the Cox regression model, with the incidence of T2DM as the dependent variable. As shown in Fig. 3, the T1–C3 (HR, 1.56; 95% CI, 1.16–2.10; P = 0.003), T2–C2 (HR, 1.53; 95% CI, 1.13–2.05; P = 0.005), and T2–C3 (HR, 1.42; 95% CI, 1.05–1.91; P = 0.022) groups were at a greater risk of developing T2DM than the T3–C3 group with the longest baseline telomere length and the highest level of C16:0 (no adjustment in model 1). After models 2–5 were adjusted for other factors, the T1–C3, T2–C2, and T2–C3 groups were at a higher risk of developing T2DM than the other groups. Among subjects with normal baseline plasma glucose levels, only the T2–C2 group was at a relatively high risk of developing T2DM (Supplementary Fig. 1).

Figure 3
Figure 3

Risks for incidence of diabetes according to relative leukocyte telomere length and C16:0/MUFAs at a 9.46-year follow-up. (A) Total participants according to RTL and C16:0. (B) Total participants according to RTL and MUFAs. Data are HR (95% CIs) based on Cox regression models. Model 1: no adjustment; model 2: adjusted for age and sex; model 3: adjusted for model 2 plus general conditions, lifestyle; model 4: adjusted for model 3 plus medical history, family history, anthropometric and metabolic variables; model 5: adjusted for model 4 plus phospholipid fatty acids.

Citation: Endocrine Connections 13, 9; 10.1530/EC-24-0033

Discussion

Studies have revealed a higher prevalence of hypertension among individuals with prediabetes compared to those with normal blood glucose levels (23), which is consistent with the results of our study. Diabetes and hypertension have been identified as the most prevalent comorbid conditions (24). Moreover, hypertension has been found to be linked to elevated plasma glucose levels, and the advancement of DM and hypertension is associated with high plasma glucose levels (25), potentially elucidating the relatively elevated prevalence of hypertension among individuals with abnormal plasma glucose levels.

The correlation analyses in this study showed that RTL was negatively correlated with age, consistent with the findings of other studies (26, 27). The results also showed that RTL was positively correlated with HOMA-β and HOMA-IR and negatively correlated with QUICKI and HDL-C, which indicated an association between telomere shortening and resistance to insulin. However, inconsistencies and contradictions remain within other studies (28, 29, 30). A Russian study on telomere length and insulin resistance showed that RTL was negatively correlated with age and HOMA-IR and that men had shorter telomeres than women (29). Based on the Framingham Heart Study, insulin resistance accelerates telomere attrition in leukocytes, and telomere length is negatively correlated with age and HOMA-IR (30). Another Chinese study showed that there was no association between baseline RTL and any additional conventional diabetes risk factor at baseline (28). There are several possible reasons for this. First, the mean age of those involved in the study was 51–62 years, which was older than those in our study. The relationship between HOMA-IR and age was closely related to the turning point. The exact reasons for the age-varying associations deserve further investigation. Secondly, there was an uneven distribution of sex, and menopause may alter the trajectory of telomere attrition. Perimenopausal women are more susceptible to metabolic dysfunction because of sex hormone disturbances. In addition, RTL has been shown to be highly heritable, and this may be related to different genetic backgrounds. Some other as yet unknown factor could be involved. Hence, these findings demonstrate the differences in the correlation between HOMA-IR and telomere length across different age groups, sex, and genetic backgrounds.

The present study revealed negative correlations of C16:0, C18:1 n-9, C20:4 n-6, C20:3 n-3, MUFAs, and RTL. Multiple linear regression analysis showed that C16:0 and MUFA levels influenced RTL, which is roughly consistent with previous studies (18). A cross-sectional study found that the intake of fats and oils was borderline and negatively associated with LTL in elderly females (31). However, a review study indicated that even though most studies have demonstrated a positive relation between n-3 PUFA and LTL, the overall relation of MUFAs and PUFAs with LTL is inconsistent (32). Certain FAs have the ability to regulate various metabolic processes. Meanwhile, telomere attrition is indicative of the accumulated impact of oxidative and inflammatory stress experienced throughout an individual's lifespan. The dietary intake associated with FA varies across life stages due to differences in dietary intake across ages (33). Inconsistent results may arise from differences in the methods used to determine FA levels: some studies calculate levels based on dietary surveys (31) while others directly measure them in blood samples using GC or GC-MS (18).

To further investigate the combined effect of PPFAs and telomere length on the risk of developing T2DM, we analyzed nine combinations of circumstances. We found that subjects with shorter baseline RTL and higher C16:0 and MUFA levels were at a higher risk of developing T2DM. These results were observed after adjusting for potential confounders, including sociodemographic variables, health behaviors, and anthropometric and metabolic variables. Higher baseline RTL, C16:0, and MUFA levels increased the strength of the associations found, suggesting that these FAs and RTL might play a coordinated role in the development of impaired physical function and diabetes. In previous studies, higher levels of C16:0 are associated with higher insulin sensitivity (34, 35) and patients newly diagnosed with diabetes have significantly elevated levels of C16:0 (36). A 30-year follow-up study of Dutch and Finnish subjects (37) revealed that patients with newly developed DM had a significantly higher intake of MUFAs than normoglycemic subjects and subjects with impaired glucose tolerance in the 20 years before diagnosis. Meanwhile, multiple studies have observed that shorter telomeres are associated with an increased risk for diabetes (11, 16, 38). Our study results, in conjunction with other similar studies, indirectly suggest that the FA profile, particularly C16:0 and MUFA, along with RTL, may be associated with the etiology of T2DM. Thus, our results provide evidence for a longitudinal relationship between FAs, RTL, and glucose metabolism, suggesting that C16:0, MUFA, and RTL may be early markers of T2DM.

Limitations

This study has some limitations. First, the subjects were mostly middle-aged and elderly residents in rural areas of Northwestern China. Thus, the sample size was small and not sufficiently representative compared with other cohort studies. In addition, some subjects did not participate in the follow-up survey for various reasons, given the long follow-up period, which resulted in incomplete follow-up data and limited the choice of statistical methods. Secondly, the blood specimens were collected only during the baseline and follow-up surveys rather than at multiple time points throughout the follow-up, and the diagnosis of diabetes mellitus was also based on the fasting plasma glucose levels measured during the surveys and the time of the surveys. Therefore, the exact time points at which abnormal changes in the plasma glucose level occurred were not available, and details of the longitudinal changes in fasting plasma glucose and other indicators measured at multiple time points were not obtained during the chronic progression of diabetes mellitus in the study subjects.

Conclusion

A negative correlation was observed between the age of the participants and RTL at baseline. Baseline plasma concentrations of C16:0, C18:1 n-9, C20:4 n-6, C20:3 n-3, and MUFAs were negatively correlated with RTL. Based on the analysis of combinations, we found that subjects with shorter telomere length and higher concentrations of C16:0 and MUFAs at baseline were at a higher risk of developing T2DM. A possible relationship between shortened leukocyte telomeres and higher concentrations of certain FAs and increased T2DM in adults is suggested.

Supplementary materials

This is linked to the online version of the paper at https://doi.org/10.1530/EC-24-0033.

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Funding

The study was supported by the National Natural Science Foundation of China (82060592), the Provincial key R&D program of Ningxia (2021BEG02026), and Ningxia Medical University Program (XT2023017). The funders were not involved in study design, data collection, data analysis, data interpretation, or writing of this manuscript.

Ethics statement and consent to participate

This study was approved by the Ethics Review Board of Ningxia Medical University (Ethics ID 2018-012). Signed informed consent was obtained from all participants before the study began.

Data availability statement

The data that support the findings of this study are available upon reasonable request.

Author contribution statement

YZ and YZ designed the study and drafted the outline. JL, XL, JQ, JZ, and XL helped supervise the field activities and collected the data. CY and JL organized and analyzed the data. CY and YZ wrote the original draft and reviewed and edited the manuscript. YZ and YZ critically reviewed and revised the manuscript. All authors read and approved the final version to be published.

Acknowledgements

We thank all the participants and all the staff working for the China Northwest Natural Population Cohort: Ningxia Project (CNC-NX).

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Online ISSN:
2049-3614
Copyright:
© the author(s) 2024
Article ID:
e240033
Article Type:
Research Article
Received Date:
26 Jan 2024
Accepted Date:
22 Jul 2024
Online Publication Date:
13 Oct 2024
Keywords:
type 2 diabetes mellitus; plasma phospholipid fatty acids; leukocyte telomere length; prospective studies
  • Figure 1
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    Figure 1

    Flowchart for participants recruited.

  • Figure 2
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    Figure 2

    Risks for incidence of diabetes according to relative leukocyte telomere length at 9.46-year follow-up. (A) Total participants. (B) Participants with normal blood glucose. (C) Participants with prediabetes. Data are HR (95% CIs) based on Cox regression models. Model 1: no adjustment; model 2: adjusted for age and sex; model 3: adjusted for model 2 plus general conditions, lifestyle; model 4: adjusted for model 3 plus medical history, family history, anthropometric and metabolic variables; model 5: adjusted for model 4 plus phospholipid fatty acids.

  • Figure 3
    View in gallery
    Figure 3

    Risks for incidence of diabetes according to relative leukocyte telomere length and C16:0/MUFAs at a 9.46-year follow-up. (A) Total participants according to RTL and C16:0. (B) Total participants according to RTL and MUFAs. Data are HR (95% CIs) based on Cox regression models. Model 1: no adjustment; model 2: adjusted for age and sex; model 3: adjusted for model 2 plus general conditions, lifestyle; model 4: adjusted for model 3 plus medical history, family history, anthropometric and metabolic variables; model 5: adjusted for model 4 plus phospholipid fatty acids.

Figure 1

Flowchart for participants recruited.
Figure 2

Risks for incidence of diabetes according to relative leukocyte telomere length at 9.46-year follow-up. (A) Total participants. (B) Participants with normal blood glucose. (C) Participants with prediabetes. Data are HR (95% CIs) based on Cox regression models. Model 1: no adjustment; model 2: adjusted for age and sex; model 3: adjusted for model 2 plus general conditions, lifestyle; model 4: adjusted for model 3 plus medical history, family history, anthropometric and metabolic variables; model 5: adjusted for model 4 plus phospholipid fatty acids.
Figure 3

Risks for incidence of diabetes according to relative leukocyte telomere length and C16:0/MUFAs at a 9.46-year follow-up. (A) Total participants according to RTL and C16:0. (B) Total participants according to RTL and MUFAs. Data are HR (95% CIs) based on Cox regression models. Model 1: no adjustment; model 2: adjusted for age and sex; model 3: adjusted for model 2 plus general conditions, lifestyle; model 4: adjusted for model 3 plus medical history, family history, anthropometric and metabolic variables; model 5: adjusted for model 4 plus phospholipid fatty acids.
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