| Literature DB >> 35433791 |
Weihua Chen1,2, Shanshan Shi1,2, Yizhou Jiang3, Liling Chen1, Ying Liao1, Kaihong Chen1, Kun Huang4.
Abstract
Background: Dietary habits and dietary intake affect telomere length, a reliable marker of biological aging and a predictor of chronic disease. Riboflavin (RF) is known as a water-soluble antioxidant vitamin, but its role in telomere length maintenance has yet to be elucidated. Objective: The purpose of this study was to examine the relationship between dietary RF intake and telomere length in a nationally representative sample of adults.Entities:
Keywords: NHANES; dietary intake; female; obese; riboflavin; telomere length
Year: 2022 PMID: 35433791 PMCID: PMC9009291 DOI: 10.3389/fnut.2022.744397
Source DB: PubMed Journal: Front Nutr ISSN: 2296-861X
FIGURE 1NHANES 1999–2002 analytic sample flow diagram.
Sample size and weighted characteristics of NHANES 1999–2002.
| Total ( | Male ( | Female ( | ||
| Characters | Weighted distributions of the participants | |||
| Age, years | 60.38 ± 0.33 | 59.54 ± 0.30 | 61.15 ± 0.43 | <0.001 |
| Ethnicity, n (%) | ||||
| Mexican American | 692 (3.5) | 354 (3.7) | 338 (3.3) | 0.093 |
| Other Hispanic | 186 (5.2) | 92 (5.5) | 94 (4.9) | |
| White (non-Hispanic) | 2206 (80.3) | 1139 (80.4) | 1067 (80.2) | |
| Non-Hispanic black | 620 (8.1) | 317 (8.1) | 303 (8.2) | |
| Other | 84 (2.9) | 38 (2.3) | 46 (3.4) | |
| Education levels | ||||
| Less than high school | 1356 (22.6) | 698 (21.3) | 658 (23.9) | 0.004 |
| Completed high school | 863 (25.6) | 404 (23.5) | 459 (27.5) | |
| More than high school | 1565 (51.8) | 837 (55.2) | 728 (48.6) | |
| Alcohol consumption, n (%) | ||||
| <12/year | 2142 (99.0) | 1288 (98.5) | 1000 (99.5) | 0.135 |
| ≥12/year | 30 (1.0) | 31 (1.5) | 5 (0.5) | |
| BMI, kg/m2 | 28.53 ± 0.21 | 28.33 ± 0.17 | 28.71 ± 0.28 | 0.096 |
| <25 | 1024 (29.7) | 495 (26.0) | 529 (33.1) | <0.001 |
| 25–29.9 | 1408 (37.7) | 822 (44.2) | 586 (31.7) | |
| 30–34.9 | 735 (19.4) | 373 (20.1) | 362 (18.7) | |
| ≥35 | 473 (13.3) | 173 (9.8) | 300 (16.4) | |
| Smoking, n (%) | 2051 (54.1) | 1291 (65.0) | 760 (44.0) | <0.001 |
| CHD, n (%) | 278 (7.03) | 188 (8.92) | 90 (5.30) | 0.005 |
| CHF, n (%) | 188 (4.26) | 98 (4.20) | 90 (4.31) | 0.898 |
| DM, n (%) | 535 (11.14) | 281 (11.51) | 254 (10.80) | 0.485 |
| Riboflavin intake, mg/d | 2.05 ± 0.03 | 2.32 ± 0.04 | 1.79 ± 0.04 | <0.001 |
| Vitamin C intake, mg/d | 95.49 ± 3.05 | 99.57 ± 3.46 | 91.74 ± 3.17 | 0.006 |
| Vitamin B12 intake, mg/d | 5.06 ± 0.18 | 5.91 ± 0.24 | 4.27 ± 0.26 | <0.001 |
| Selenium intake, mcg/d | 101.98 ± 1.32 | 119.85 ± 1.92 | 85.56 ± 1.39 | <0.001 |
| Zinc intake, mg/d | 11.27 ± 0.19 | 13.12 ± 0.24 | 9.57 ± 0.22 | <0.001 |
| Copper intake, mg/d | 1.27 ± 0.02 | 1.44 ± 0.03 | 1.12 ± 0.02 | <0.001 |
| Dietary fiber intake, gm/d | 16.10 ± 0.36 | 17.84 ± 0.49 | 14.49 ± 0.31 | <0.001 |
| Telomere length, T/S ratio | 0.97 ± 0.02 | 0.96 ± 0.02 | 0.99 ± 0.02 | 0.014 |
NHANES, National Health and Nutrition Examination Survey; BMI, body mass index; CHD, Coronary heart disease; CHF, Congestive heart failure; DM, Diabetes mellitus. Mean and standard deviation were presented for continuous variables, number and proportion were presented for categorical variables.
The association between riboflavin intake and telomere length among all/subgroup participants.
| Participants | Models | β and 95% CI | ||
| All participants | Model 1 | 0.011 (0.001, 0.021) | 0.037 | – |
| Model 2 | 0.012 (0.002, 0.023) | 0.033 | ||
| Model 3 | 0.014 (−0.002, 0.029) | 0.107 | ||
|
| ||||
| Female | Model 1 | 0.020 (0.005, 0.035) | 0.014 | 0.044 |
| Model 2 | 0.019 (0.004, 0.035) | 0.020 | ||
| Model 3 | 0.029 (0.004, 0.054) | 0.046 | ||
| Male | Model 1 | 0.012 (0.000, 0.024) | 0.071 | |
| Model 2 | 0.008 (−0.005, 0.021) | 0.240 | ||
| Model 3 | −0.001 (−0.019, 0.017) | 0.935 | ||
|
| ||||
| Normal (<25 kg/m2) | Model 1 | 0.005 (−0.010, 0.020) | 0.501 | 0.029 |
| Model 2 | 0.001 (−0.016, 0.018) | 0.928 | ||
| Model 3 | 0.007 (−0.024, 0.037) | 0.684 | ||
| Overweight (25–29.9 kg/m2) | Model 1 | 0.015 (−0.001, 0.030) | 0.070 | |
| Model 2 | 0.019 (0.002, 0.036) | 0.040 | ||
| Model 3 | 0.005 (−0.018, 0.027) | 0.695 | ||
| Obese (30–34.9 kg/m2) | Model 1 | 0.013 (−0.008, 0.034) | 0.230 | |
| Model 2 | 0.012 (−0.014, 0.038) | 0.384 | ||
| Model 3 | 0.003 (−0.050, 0.054) | 0.906 | ||
| Severe-obese (≥35 kg/m2) | Model 1 | 0.010 (−0.006, 0.026) | 0.236 | |
| Model 2 | 0.012 (−0.008, 0.031) | 0.254 | ||
| Model 3 | 0.043 (0.014, 0.072) | 0.027 | ||
CI, confidence interval; BMI, body mass index; Model 1 included only the exposure variable, riboflavin intake; Model 2 was additionally adjusted for age, sex, and ethnicity; Model 3 was further adjusted for education levels, smoking, alcohol consumption, BMI, vitamin C intake, vitamin B12 intake, selenium intake, zinc intake, copper intake, dietary fiber intake, coronary heart disease, congestive heart failure and diabetes mellitus.
FIGURE 2The dose-response relationship between dietary riboflavin intake and telomere length. Point estimates (solid line) and 95% confidence intervals (dashed lines) were estimated by restricted cubic splines analysis with knots placed at the 5th, 50th, and 95th percentile (minimum as the reference).
FIGURE 3The dose-response relationship between riboflavin intake and telomere among sex and obese subgroup. (A) Non-obese female participant. (B) Obese female participant. (C) Non-obese male participant. (D) Obese male participant. Models were adjusted for age, ethnicity, education levels, smoking, alcohol consumption, BMI, vitamin C intake, vitamin B12 intake, selenium intake, zinc intake, copper intake, dietary fiber intake, coronary heart disease, congestive heart failure, and diabetes mellitus.
FIGURE 4The dose-response relationship between riboflavin intake and telomere in different riboflavin levels among female and obese subgroup. (A) Low riboflavin intake and non-obese group. (B) Low riboflavin intake and obese group. (C) Normal riboflavin intake and non-obese group. (D) Normal riboflavin intake and obese group. Models were adjusted for age, ethnicity, Vitamin C intake, smoking, and alcohol consumption.