Abstract
Background
This study explored the correlation between hemoglobin (HB) and diabetic retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM), and the potential impact of disease duration.
Methods
Data were collected from the National Health and Nutrition Examination Survey (NHANES) (2001–2018) and the National Metabolic Management Center (MMC) of Fujian Medical University Union Hospital (2018–2023). DR risk factors were identified by univariate analysis. The HB-DR correlation was explored by multivariate logistic regression and analyzed by generalized additive model and smoothed curve fitting. The impact of diabetes mellitus (DM) duration on the correlation was examined by interaction tests.
Results
Data of 5,089 and 1,893 participants were respectively recruited from the NHANES and MMC databases. In both databases, HB levels were negatively correlated with DR. In the MMC, the negative association persisted in subgroup stratification of sex, age, and body mass index, with the normal weight group reporting a U-shaped relationship at an inflection point of 12.7 (g/dL). The interaction between DM duration and HB significantly impacted the DR probability.
Conclusions
DM duration may impact the association between HB and DR risk and a low HB level indicates a significant DR risk in patients with T2DM, especially in those with a short DM duration, highlighting the necessity of hemoglobin detection and anemia intervention in the treatment of patients with T2DM.
Clinical trial number
Not applicable.
Keywords: Hemoglobin, Diabetic retinopathy, Type 2 diabetes mellitus, Duration of diabetes mellitus
Introduction
With the advances in socioeconomy and everyday lifestyle, diabetes mellitus (DM), a syndrome characterized by hyperglycemia as a result of insufficient insulin action and/or secretion, has become globally prevalent. According to the report of International Diabetes Federation (IDF) in 2021, there are currently 537 million patients with DM in the age range of 20–79 years old in the world, which is predicted to reach 783 million in 2045 [1]. In general, diabetes is often comorbid with a range of complications, which are traditionally divided into macrovascular complications [e.g., cardiovascular disease (CVD)] and microvascular complications (e.g., those affecting the kidney, retina and nervous system) [2]. Studies demonstrate that type 2 diabetes mellitus (T2DM) can significantly elevate risks of multiple comorbidities, reporting 9-fold higher incidence of cancerogenesis, 4-fold greater possibility of progression of liver diseases to cirrhosis, and 3-fold higher risk of macular degeneration [3]. As a retinal complication of diabetes, diabetic retinopathy (DR) disrupts the normal interactions between the neural and vascular components of the retina, leading to increased vascular permeability, neovascularization, and partial loss of nerve function [4]. DR has been ranked as one of the primary causes of avoidable visual impairment and blindness in people aged 50 years and older worldwide [5]. With the increasing incidence rate of diabetes and parallel rise in DR incidence, the global prevalence and disease burden of DR are estimated to increase significantly in the coming decades, from approximately 103 million people in 2020 to 130 million people in 2030 and 161 million people in 2045 [6], which will impose a high economic and medical burden. Currently, the etiology of DR has been extensively explored [7–10]. However, controversies remain and much is left unanswered. For instance, how to monitor and treat early DR is still a clinical focus. Therefore, due attention and research should be undertaken to facilitate early prevention and intervention in the hope of mitigating the challenges posed by DR.
Pathophysiologically, patients with diabetes are highly vulnerable to diabetes-related complications when they are inflicted with anemia [11, 12]. Previous studies have evidenced that the reduction of oxygen carrying capacity can aggravate tissue hypoxia, which is closely linked with the development of vision-threatening diabetic retinopathy (VTDR) [13] and that a lower HB level is significantly correlated with a severe risk of DR and retinal ischemia [14, 15]. These findings indicate that lower HB may exert a significant impact on DR development. Meanwhile, available literature has demonstrated that DM duration may serve as an influential risk factor for DR development [16, 17]. Nevertheless, the existent reports have recruited a small number of participants and have not further stratified the correlation between HB and DR by age, gender, body mass index (BMI) and other demographic indices. Furthermore, it remains obscure whether the reported correlation is affected by the duration of DM. Given the differences in study populations and ethnic backgrounds, and the research gaps we identified, the current cross-sectional study attempted to explore the correlation of HB with DR occurrence in a population with type 2 diabetes mellitus (T2DM) from 2 different databases in the United States and China, and the potential impact of DM duration on the suspected correlation.
Materials and methods
Data source and study population
The National Health and Nutrition Examination Survey (NHANES) includes studies designed to assess the health and nutritional status of adults and children in the United States. Its questionnaire consists of socio-economic, demographic, health-related, and dietary questions and examination records cover dental, medical, and physiologic measurements, as well as laboratory tests performed by trained professionals. All data are accessible on the website (https://www.cdc.gov/nchs/nhanes). The National Metabolic Management Center (MMC) of Fujian Medical University Union Hospital, Fujian Province, China is the only demonstration center of screening and prevention project of DR in Fujian Province, which provides an integrated management of metabolic diseases such as diabetes by establishing a comprehensive evaluation and regular follow-up system for patients with metabolic diseases. The data involve a standardized questionnaire, laboratory tests, physical and anthropometric examination, and evaluation of diabetes-related complications.
Each participant provided written informed consent prior to inclusion in the NHANES database, which was reviewed and approved by the Ethics Review Committee of the National Health Statistics Center. The data were anonymized to facilitate data accessibility for public use. The study protocol was approved by the Ethics Committee of Fujian Medical University Union Hospital (Ethics number: No. 2018KY072), and participants provided written informed consent for personal information collection.
Study design
The definition of T2DM referred to the American Diabetes Association guidelines (ADA) [18] and a self-report questionnaire: (1) glycated hemoglobin (HbA1c) ≥ 6.5%; (2) fasting glucose (FBG) ≥ 7 mmol/L (126 mg/dl); (3) 2-hplasma glucose ≥ 11.1 mmol/L during an oral glucose tolerance test; (4) self-report questionnaire data indicating physician diagnosis of diabetes; (5) blood glucose lowered by current use of insulin or diabetes pills; and (6) pregnant women or an age of < 30 years old when first-informed of diabetes. DR was examined by fundus photography and diagnosed by a fundus specialist. It was grouped into five categories by the International Clinical Diabetic Retinopathy Grading Criteria [19]: (1) no obvious retinopathy; (2) mild non-proliferative diabetic retinopathy (NPDR); (3) moderate NPDR; (4) severe NPDR; and (5) proliferative diabetic retinopathy (PDR). This study designated DR as any of the other four categories except the first one.
In the NHANES database, a total of 91,351 people, followed from 2001 to 2018, were enrolled in this study. The exclusion criteria included: (1) patients with a non T2DM diabetes status (n = 83,146); (2) missing data for DR status (n = 2,179); and (3) missing data for HB status (n = 928). A total of 5,089 eligible people were recruited, including non-DR group (n = 3,978) and DR group (n = 1,111). In the MMC, the data of 3,000 patients followed from 2018 to 2023 were also collected. The exclusion criteria included: (1) patients with a non T2DM diabetes status (n = 132), (2) missing data for DR status (n = 711); and (3) missing data for HB status (n = 264). A total of 1,893 eligible participants were enrolled and categorized into non-DR group (n = 1,179) and DR group (n = 714). A flow chart of the screening process in both databases is shown in Fig. 1.
Fig. 1.
Flowchart of study population in two databases. A Flowchart of study population NHANES (2001–2018). B Flowchart of study population MMC (2018–2023)
Collection of relevant variables
Based on the current research and clinical experience, clinical and laboratory indicators that may affect the progression of DR were screened from the two databases, including age, gender, education level, income level, diastolic blood pressure (DBP), systolic blood pressure (SBP), BMI, smoking, drinking, fasting blood glucose (FBG), duration of DM, Hemoglobin A1c (HbA1c), HB, albumin (ALB), total cholesterol (TC), triglycerides (TG), low-density lipoprotein-cholesterol (LDL-c), high-density lipoprotein-cholesterol (HDL-c), serum uric acid (SUA), hypersensitive C-reactive protein (hs-CRP), blood urea nitrogen (BUN), hyperuricemia, serum creatinine (Scr), estimated glomerular filtration rate (eGFR), urinary albumin, urinary creatinine, urinary albumin/creatinine ratio (UACR), thyroid stimulating hormone (TSH), free tri-iodothyronine (FT3), free tetra-iodothyronine (FT4), coronary heart disease (CHD), hypertension, use of antidiabetic medications, and insulin therapy.
Statistical analysis
Statistical analyses were performed with EmpowerStats software (http://www.enablestats.com/en/, X & Y solution, Inc., Boston, MA) and R language 4.1.2 (http://www.R-proje ct.org). Normally distributed data were presented as mean ± standard deviation (Mean ± SD), with inter-group differences assessed by the two independent samples t-test; non-normally distributed data were expressed as median [IQR], with differences between groups evaluated by the two independent samples Wilcoxon rank sum test. Categorical variables were described as percentages (%) and tested by the chi-square test. The relationship between HB levels and DR was examined by a stratified multivariate logistic regression. No adjustment of variables was performed for Model 1; adjustments of BMI, gender, age, and DM duration were made for Model 2; and adjustments of all covariates was for Model 3. Nonlinear relationships were assessed by generalized additive model (GAM) and smooth curve fitting. For a nonlinear correlation, a two-stage linear regression model was built based on the smoothed plot and the fitted model with the largest likelihood value was identified by the recursive experimental method for the automatic calculation of the inflection point. The impact of DM duration on the HB-DR association was evaluated by an interaction test. Statistical significance was set at p < 0.05.
Results
The characteristics of participants
The demographic characteristics of non-DR and DR are shown in Tables 1 and 2. In the NHANES, compared with the non-DR counterparts, the patients with DR reported a significantly greater use of antidiabetic medications, longer DM duration, higher levels of FBG, HbA1c, BUN, Scr, urinary albumin, UACR, increased proportion of hyperuricemia and CHD, but significantly lower levels of HB, ALB, urinary creatinine, eGFR, FT3, and decreased proportion of insulin therapy (Table 1). In MMC, compared with the non-DR, patients with DR showed a markedly longer DM duration and higher proportion of smoking, HbA1c, BUN, Scr, urinary albumin, UACR, TC, LDL-c, hyperuricemia, and increased use of antidiabetic medications and insulin, but a significantly lower proportion of HB, ALB, TG, urinary creatinine, eGFR, and FT3 (Table 2).
Table 1.
Baseline characteristics of participants in NHANES
| NHANES data (n = 5089) |
P | ||
|---|---|---|---|
| Non-DR | DR | ||
| (N = 3978) | (N = 1111) | ||
| Age (years) | 64.0 [54.0,72.0] | 64.0 [55.0,72.0] | 0.736 |
| Gender (%) | 0.729 | ||
| Female | 1909 (48.0%) | 526 (47.3%) | |
| Male | 2069 (52.0%) | 585 (52.7%) | |
| Education level (%) | 0.005 | ||
| < High school | 1391 (35.0%) | 440 (39.6%) | |
| ≥ High school | 2587 (65.0%) | 671 (60.4%) | |
| Income level (%) | < 0.001 | ||
| Low | 977 (24.6%) | 232 (20.9%) | |
| Middle | 1364 (34.3%) | 452 (40.7%) | |
| High | 1637 (41.2%) | 427 (38.4%) | |
| BMI (kg/m2) | 30.9 [27.0,35.9] | 31.0 [26.9,36.4] | 0.664 |
| BMI group (%) | 0.239 | ||
| Normal | 538 (13.5%) | 161 (14.5%) | |
| Overweight | 2214 (55.7%) | 636 (57.2%) | |
| Obese | 1226 (30.8%) | 314 (28.3%) | |
| Smoking status (%) | 0.042 | ||
| No | 2780 (69.9%) | 812 (73.1%) | |
| Yes | 1198 (30.1%) | 299 (26.9%) | |
| Drinking status (%) | 0.443 | ||
| No | 1855 (46.6%) | 503 (45.3%) | |
| Yes | 2123 (53.4%) | 608 (54.7%) | |
| SBP (mmHg) | 129 [118,143] | 132 [119,146] | 0.058 |
| DBP (mmHg) | 69.3 [60.7,76.7] | 68.0 [58.7,76.0] | 0.002 |
| FBG (mmol/L) | 7.83 [6.44,10.1] | 8.55 [6.88,11.7] | < 0.001 |
| DM duration (years) | 9.00 [4.00,16.0] | 15.0 [7.00,24.0] | < 0.001 |
| HbA1c (%) | 6.90 [6.20,7.90] | 7.30 [6.50,8.70] | < 0.001 |
| HB (g/L) | 13.8 [12.7,14.9] | 13.4 [12.4,14.5] | < 0.001 |
| ALB (g/L) | 41.0 [39.0,43.0] | 40.0 [38.0,43.0] | < 0.001 |
| TG (mmol/L) | 1.69 [1.07,2.96] | 1.76 [1.02,3.04] | 0.816 |
| TC (mmol/L) | 4.60 [3.90,5.40] | 4.53 [3.88,5.51] | 0.682 |
| HDL-c (mmol/L) | 1.19 [1.01,1.45] | 1.22 [1.01,1.45] | 0.542 |
| LDL-c (mmol/L) | 2.35 [1.76,3.00] | 2.33 [1.73,3.03] | 0.517 |
| hs-CRP (mg/L) | 1.70 [0.700,3.92] | 1.90 [0.690,4.45] | 0.065 |
| SUA (µmol/L) | 327 [274,399] | 339 [280,410] | 0.056 |
| BUN (mmol/L) | 5.36 [4.28,7.14] | 6.07 [4.46,8.57] | < 0.001 |
| Scr (µmol/L) | 79.6 [65.4, 98.1] | 85.7 [67.6, 115.8] | < 0.001 |
| eGFR | 79.5 [62.2,98.7] | 72.8 [51.3,97.3] | < 0.001 |
| Albumin urine (mg/L) | 13.6 [6.00,42.1] | 24.0 [8.00,96.5] | < 0.001 |
| Creatinine urine (mg/dl) | 102 [64.0,153] | 94.0 [58.0,144] | < 0.001 |
| UACR (mg/g) | 16.2 [7.49,78.5] | 41.2 [10.5,242] | < 0.001 |
| FT3 (pmol/l) | 4.60 [4.16, 4.93] | 4.47 [4.16, 4.91] | < 0.001 |
| FT4 (pmol/l) | 11.6 [9.90, 13.0] | 11.6 [10.2, 13.3] | 0.098 |
| TSH (mIU/L) | 1.59 [0.968,2.57] | 1.57 [0.934,2.51] | 0.334 |
| Hypertension (%) | 0.002 | ||
| No | 133 (3.3%) | 17 (1.5%) | |
| Yes | 3845 (96.7%) | 1094 (98.5%) | |
| Hyperuricemia (%) | 0.002 | ||
| No | 3243 (81.5%) | 860 (77.4%) | |
| Yes | 735 (18.5%) | 251 (22.6%) | |
| CHD (%) | < 0.001 | ||
| No | 3536 (88.9%) | 933 (84.0%) | |
| Yes | 442 (11.1%) | 178 (16.0%) | |
| Antidiabetic medications (%) | < 0.001 | ||
| No | 3113 (78.3%) | 578 (52.0%) | |
| Yes | 865 (21.7%) | 533 (48.0%) | |
| Insulin therapy (%) | < 0.001 | ||
| No | 990 (24.9%) | 361 (32.5%) | |
| Yes | 2988 (75.1%) | 750 (67.5%) | |
| HB group (%) | < 0.001 | ||
| Q1 | 948 (23.8%) | 366 (32.9%) | |
| Q2 | 998 (25.1%) | 286 (25.7%) | |
| Q3 | 1035 (26.0%) | 249 (22.4%) | |
| Q4 | 997 (25.1%) | 210 (18.9%) | |
BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; FBG: fasting blood glucose; HbA1c: glycosylated hemoglobin A1c; HB: hemoglobin; ALB: albumin; TG: triglyceride; TC: total cholesterol, HDL: high-density lipoprotein-cholesterol, LDL: low-density lipoprotein-cholesterol, hs-CRP: hypersensitive C-reaction protein; SUA: serum uric acid; BUN: blood urea nitrogen; Scr: serum creatinine; eGFR: estimated glomerular filtration rate; UACR: urinary albumin/creatinine ratio; CHD: coronary heart disease
Table 2.
Baseline characteristics of participants in MMC
| MMC data (n = 1893) |
P | ||
|---|---|---|---|
| Non-DR | DR | ||
| (N = 1179) | (N = 714) | ||
| Age (years) | 59.0 [52.0,66.0] | 60.0 [52.0,67.0] | 0.274 |
| Gender (%) | 0.389 | ||
| Female | 459 (38.9%) | 263 (36.8%) | |
| Male | 720 (61.1%) | 451 (63.2%) | |
| Education level (%) | 0.941 | ||
| < High school | 672 (57.0%) | 409 (57.3%) | |
| ≥ High school | 507 (43.0%) | 305 (42.7%) | |
| Income level (%) | 0.571 | ||
| Low | 194 (16.5%) | 131 (18.3%) | |
| Middle | 477 (40.5%) | 282 (39.5%) | |
| High | 508 (43.1%) | 301 (42.2%) | |
| BMI (kg/m2) | 24.6 [22.8,27.0] | 24.6 [22.3,27.0] | 0.236 |
| BMI group (%) | |||
| Normal | 614 (52.1%) | 381 (53.4%) | 0.817 |
| Overweight | 411 (34.9%) | 239 (33.5%) | |
| Obese | 128 (10.9%) | 79 (11.1%) | |
| Smoking status (%) | 0.019 | ||
| No | 723 (61.3%) | 398 (55.7%) | |
| Yes | 456 (38.7%) | 316 (44.3%) | |
| Drinking status (%) | 0.473 | ||
| No | 657 (55.7%) | 385 (53.9%) | |
| Yes | 522 (44.3%) | 329 (46.1%) | |
| SBP (mmHg) | 127 [115,140] | 128 [116,140] | 0.535 |
| DBP (mmHg) | 73.0 [65.0,80.0] | 73.0 [65.0,80.0] | 0.78 |
| FBG (mmol/L) | 8.02 [6.44,10.7] | 8.04 [6.46,10.5] | 0.955 |
| DM duration (years) | 9.00 [4.00,15.0] | 9.50 [2.0,15.0] | 0.045 |
| HbA1c (%) | 8.50 [7.30,10.5] | 9.00 [7.60,10.7] | < 0.001 |
| HB (g/L) | 14.1 [13.0,15.2] | 13.6 [12.3,14.8] | < 0.001 |
| ALB (g/L) | 42.7 [39.8,45.1] | 41.5 [37.9,44.4] | < 0.001 |
| TG (mmol/L) | 1.50 [1.07,2.21] | 1.44 [1.00,2.09] | 0.028 |
| TC (mmol/L) | 4.47 [3.72,5.36] | 4.68 [3.79,5.53] | 0.002 |
| HDL-c (mmol/L) | 1.06 [0.88,1.32] | 1.09 [0.880,1.33] | 0.294 |
| LDL-c (mmol/L) | 2.90 [2.18,3.60] | 3.10 [2.31,3.89] | < 0.001 |
| hs-CRP (mg/L) | 1.70 [0.660,3.95] | 1.80 [0.693,4.15] | 0.427 |
| SUA (µmol/L) | 322 [266,388] | 321 [264,400] | 0.438 |
| BUN (mmol/L) | 5.40 [4.40,6.50] | 5.60 [4.70,7.10] | < 0.001 |
| Scr (µmol/L) | 69.0 [59.0,83.0] | 72.0 [59.3,87.0] | 0.005 |
| eGFR | 98.9 [76.2,122] | 94.0 [71.5,120] | 0.023 |
| Albumin urine (mg/L) | 11.3 [4.93,31.3] | 18.1 [6.46,101] | < 0.001 |
| Creatinine urine (mg/dl) | 84.6 [55.8,136] | 72.7 [49.0,107] | < 0.001 |
| UACR (mg/g) | 11.1 [5.85,34.9] | 24.5 [7.87,142] | < 0.001 |
| FT3 (pmol/l) | 5.00 [4.58,5.39] | 4.80 [4.37,5.26] | < 0.001 |
| FT4 (pmol/l) | 11.6 [10.5,13.0] | 11.7 [10.6,13.1] | 0.391 |
| TSH (mIU/L) | 1.52 [1.03,2.32] | 1.57 [1.06,2.44] | 0.382 |
| Hypertension (%) | 0.60 | ||
| No | 432 (36.6%) | 271 (38.0%) | |
| Yes | 747 (63.4%) | 443 (62.0%) | |
| Hyperuricemia (%) | 0.036 | ||
| No | 929 (78.8%) | 532 (74.5%) | |
| Yes | 250 (21.2%) | 182 (25.5%) | |
| CHD (%) | 0.999 | ||
| No | 1072 (90.9%) | 650 (91.0%) | |
| Yes | 107 (9.1%) | 64 (9.0%) | |
| Antidiabetic medications (%) | 0.016 | ||
| No | 413 (35.0%) | 211 (29.6%) | |
| Yes | 766 (65.0%) | 503 (70.4%) | |
| Insulin therapy (%) | < 0.001 | ||
| No | 752 (63.8%) | 332 (46.5%) | |
| Yes | 427 (36.2%) | 382 (53.5%) | |
| HB group (%) | < 0.001 | ||
| Q1 | 253 (21.5%) | 224 (31.4%) | |
| Q2 | 298 (25.3%) | 190 (26.6%) | |
| Q3 | 304 (25.8%) | 158 (22.1%) | |
| Q4 | 324 (27.5%) | 142 (19.9%) | |
BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; FBG: fasting blood glucose; HbA1c: glycosylated hemoglobin A1c; HB: hemoglobin; ALB: albumin; TG: triglyceride; TC: total cholesterol, HDL: high-density lipoprotein-cholesterol, LDL: low-density lipoprotein-cholesterol, hs-CRP: hypersensitive C-reaction protein; SUA: serum uric acid; BUN: blood urea nitrogen; Scr: serum creatinine; eGFR: estimated glomerular filtration rate; UACR: urinary albumin/creatinine ratio; CHD: coronary heart disease
Association between hemoglobin levels and DR
The correlation between HB levels and the occurrence of DR was evaluated in 3 models by logistic regression (Table 3). Model 1, not adjusted for confounders, reported a negative association between HB and DR in both the NHANES database and the MMC database (NHANES: OR = 0.86, 95%CI, 0.83–0.90, p < 0.001; MMC: OR = 0.98, 95%CI, 0.98–0.99, p < 0.001); model 2, adjusted for BMI, gender, age, and DM duration, revealed that the significant negative association between HB levels and DR occurrence persisted in both databases (NHANES: OR = 0.86, 95%CI, 0.82–0.90, p < 0.001; MMC: OR = 0.98, 95%CI, 0.98–0.99, p < 0.001); and in model 3, after the adjustment of all the included covariates, this negative correlation remained (NHANES: OR = 0.92, 95%CI, 0.88–0.98, p = 0.005; MMC: OR = 0.99, 95%CI, 0.98–0.99,p = 0.017). The sensitivity analysis, with HB treated as a categorical variable (quartiles), showed that the significant negative correlation trend was still present in all 3 models. The curve-fit plots of the association of HB levels with DR occurrence, after the adjustment of all covariates in the 2 databases, further validated the negative nonlinear relationship between the two (Fig. 2).
Table 3.
Association between hemoglobin levels and diabetic retinopathy
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| OR [95% CI] | P | OR [95% CI] | P | OR [95% CI] | P | |
| NHANES | ||||||
| HB | 0.86 [0.83, 0.90] | < 0.001 | 0.86 [0.82, 0.90] | < 0.001 | 0.92 [0.88, 0.98] | 0.005 |
| HB(Quartile) | ||||||
| Q1 | Ref | Ref | Ref | Ref | Ref | Ref |
| Q2 | 0.74 [0.62, 0.89] | 0.001 | 0.77 [0.64, 0.93] | 0.006 | 0.94 [0.77, 1.14] | 0.511 |
| Q3 | 0.62 [0.52, 0.75] | < 0.001 | 0.63 [0.52, 0.76] | < 0.001 | 0.79 [0.63, 0.99] | 0.029 |
| Q4 | 0.55 [0.45, 0.66] | < 0.001 | 0.53 [0.43, 0.66] | < 0.001 | 0.70 [0.55, 0.90] | 0.005 |
| p for trend | 0.85 [0.77, 0.87] | < 0.001 | 0.81 [0.75, 0.87] | < 0.001 | 0.89 [0.82, 0.96] | 0.002 |
| MMC | ||||||
| HB | 0.98 [0.98, 0.99] | < 0.001 | 0.98 [0.98, 0.99] | < 0.001 | 0.99 [0.98, 0.99] | 0.017 |
| HB(Quartile) | ||||||
| Q1 | Ref | Ref | Ref | Ref | Ref | Ref |
| Q2 | 0.72 [0.56, 0.93] | 0.012 | 0.72 [0.56, 0.93] | 0.013 | 0.91 [0.69, 1.20] | 0.508 |
| Q3 | 0.59 [0.45, 0.76] | < 0.001 | 0.59 [0.45, 0.77] | < 0.001 | 0.82 [0.60, 1.09] | 0.157 |
| Q4 | 0.50 [0.38, 0.65] | < 0.001 | 0.50 [0.38, 0.65] | < 0.001 | 0.72 [0.52, 0.98] | 0.038 |
| p for trend | 0.79 [0.73, 0.86] | < 0.001 | 0.79 [0.73, 0.86] | < 0.001 | 0.89 [0.81, 0.99] | 0.029 |
OR: Odds ratio; CI: Confidence interval; Model 1: no covariates were adjusted; Model 2: age, sex, BMI and duration were adjusted; Model 3: all covariates were adjusted
Fig. 2.
The association between hemoglobin levels and the probability of diabetic retinopathy. A The association between hemoglobin levels and the probability of DR in NHANES after adjusting for all covariates. B The association between hemoglobin levels and the probability of DR in MMC after adjusting for all covariates. The solid red line represents the smooth curve fit between variables. Blue bands represent the 95% confidence interval from the fit. HB: hemoglobin; DR: diabetic retinopathy
Subgroup analysis
The subgroup analysis stratified by gender, age, and BMI in MMC was further performed (Table 4). In the gender subgroup, a significant negative correlation between HB level and DR was evident in both females and males (p < 0.05). In the age subgroup, no significant association between HB level and DR was found in the subgroups of people aged < 40 years old and > 70 years old (p > 0.05), whereas a marked negative association was observed in three subgroups of people at the age ranges of 40–49/50–59/60–69 years (p < 0.05). In the BMI subgroup, HB level showed a significant negative correlation with DR (p < 0.05), whether in the normal weight, overweight or obese groups.
Table 4.
Association between hemoglobin (g/dl) and stratified by gender, age, and BMI in MMC
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| OR [95% CI] | P | OR [95% CI] | P | OR [95% CI] | P | |
| Stratified by gender | ||||||
| Male | 0.98 [0.98, 0.99] | < 0.001 | 0.98 [0.98, 0.98] | < 0.001 | 0.99 [0.98, 0.99] | 0.019 |
| Female | 0.98 [0.98, 0.99] | < 0.001 | 0.98 [0.97, 0.98] | < 0.001 | 0.99 [0.98, 0.99] | 0.010 |
| Stratified by age | ||||||
| < 40 years | 0.98 [0.98, 0.99] | < 0.001 | 0.98 [0.98, 0.99] | < 0.001 | 0.99 [0.98, 1.01] | 0.067 |
| 40–49 years | 0.98 [0.98, 0.99] | < 0.001 | 0.98 [0.97, 0.98] | < 0.001 | 0.99 [0.98, 0.99] | 0.006 |
| 50–59 years | 0.98 [0.97, 0.98] | < 0.001 | 0.98 [0.97, 0.98] | < 0.001 | 0.99 [0.98, 0.99] | 0.005 |
| 60–69 years | 0.98 [0.98, 0.99] | < 0.001 | 0.98 [0.98, 0.99] | < 0.001 | 0.99 [0.98, 0.99] | 0.023 |
| > 70 years | 0.98 [0.98, 0.99] | < 0.001 | 0.98 [0.98, 0.99] | < 0.001 | 0.99 [0.98, 1.01] | 0.088 |
| Stratified by BMI | ||||||
| Normal | 0.98 [0.97, 0.98] | < 0.001 | 0.98 [0.97, 0.98] | < 0.001 | 0.99 [0.98, 0.99] | 0.013 |
| Overweight | 0.98 [0.97, 0.98] | < 0.001 | 0.98 [0.97, 0.98] | < 0.001 | 0.99 [0.98, 0.99] | 0.011 |
| Obese | 0.98 [0.97, 0.98] | < 0.001 | 0.98 [0.97, 0.99] | < 0.001 | 0.99 [0.98, 0.99] | 0.015 |
OR: Odds ratio; CI: Confidence interval; Model 1: no covariates were adjusted; Model 2: age, sex, BMI and duration were adjusted; Model 3: all covariates were adjusted
In addition, smoothed curve fitting and generalized additive modeling were performed to depict the nonlinear relationship. The results showed a non-linear relationship curve between HB levels and DR in the male group when subgrouped by gender, with a turning point of 12.5 (g/dL) and an insignificant log-likelihood ratio (p = 0.107) (Fig. 3A; Table 5). When subgrouped by BMI, a U-shaped curve was found in the normal weight group, with an inflection point of 12.7 (g/dL) and a significant log-likelihood ratio (p = 0.022). A marked negative correlation between HB levels and DR was present on the left side of the inflection point (OR = 0.96, 95% CI, 0.94–0.98, p = 0.001), but not on the right side (p = 0.603) (Fig. 3B; Table 5).
Fig. 3.
Relationship between hemoglobin levels and diabetic retinopathy in MMC. A Relationship between hemoglobin levels and diabetic retinopathy stratified by gender. B Relationship between hemoglobin levels and diabetic retinopathy stratified by BMI. C Relationship between hemoglobin levels and diabetic retinopathy stratified by DM duration
A B
Table 5.
Threshold effect analysis of hemoglobin levels on diabetic retinopathy in MMC
| Odds ratios of diabetic retinopathy | Adjusted OR [95% CI] | P |
|---|---|---|
| Male | ||
| Inflection point. | 12.5 | |
| Hemoglobin < 12.5 (g/dL) | 0.98 [0.96, 1.07] | 0.193 |
| Hemoglobin > 12.5 (g/dL) | 1.01 [0.99, 1.02] | 0.222 |
| Log likelihood ratio | 0.11 | |
| Normal weight | ||
| Inflection point | 12.7 | |
| Hemoglobin < 12.7 (g/dL) | 0.96 [0.94, 0.98] | 0.001 |
| Hemoglobin > 12.7 (g/dL) | 0.99 [0.98, 1.01] | 0.603 |
| Log likelihood ratio | 0.02 |
OR: Odds ratio; CI: Confidence interval. All covariates were adjusted
The impact of HB-DM duration interaction on the probability of DR
The duration of DM was categorized into short DM duration group (DM duration < 10 years) and long DM duration group (DM duration ≥ 10 years). The analysis showed that both the long and short DM duration exhibited a significant negative correlation trend in the MMC database and that the short DM duration group reported a notably more drastic downward trend than the long DM duration group (Fig. 3C). The results showed that the HB-DM duration interaction was statistically significant in the three models (Model 1: p = 0.008; Model 2: p = 0.009; Model 3: p = 0.017) (Table 6). These findings suggest that DM duration may impact the HB-DR association in patients with T2DM in the MMC database.
Table 6.
Effect of DM duration on the relationship between HB and the probability of DR in MMC
| OR [95% CI] | P | P for interaction | |
|---|---|---|---|
| Model 1 | |||
| HB ‡ DM duration < 10 years | 0.97 [0.96, 0.98] | < 0.001 | 0.008 |
| HB ‡ DM duration ≥ 10 years | 0.98 [0.98, 0.99] | 0.026 | |
| Model 2 | |||
| HB ‡ DM duration < 10 years | 0.97 [0.96, 0.98] | < 0.001 | 0.009 |
| HB ‡ DM duration ≥ 10 years | 0.98 [0.98, 0.99] | 0.024 | |
| Model 3 | |||
| HB ‡ DM duration < 10 years | 0.98 [0.96, 0.99] | < 0.001 | 0.017 |
| HB ‡ DM duration ≥ 10 years | 0.99 [0.98, 1.01] | 0.543 |
OR: Odds ratio; CI: Confidence interval; HB: hemoglobin; DM: diabetes mellitus
Discussion
Our study collected both NHANES data from the United States and MMC data from China. After adjusting potential covariates in both databases by multivariate logistic regression, the study reported a negative correlation between HB level and the probability of DR in patients with T2DM. We further stratified the MMC database and found a U-shaped association between HB level and DR in people with normal weight. The interaction analysis revealed that the relationship between HB level and DR in the MMC database may be impacted by the duration of DM.
Currently, available literature demonstrates that DR development is closely associated with some risk factors, such as DM duration, HbA1c, Scr, UACR, and hyperuricemia [16, 17, 20–23]. In our study, a similar significant association was consistently found in both databases. Moreover, in both 2 databases, HB was negatively correlated with DR occurrence, which has been previously noted by several studies. For example, one retrospective cohort study using medical claims data from a large US insurance company has noted that anemia may be a risk factor for progression from non-proliferative DR to vision-threatening DR [13]. Similarly, in China, retrospective studies have reported that, independently of other risk factors, low HB levels are closely associated with DR occurrence [24] and that HB may independently serve as a risk factor for DR development in elderly patients with T2DM. Consistently, our study found similar findings. However, previous studies recruited a small number of participants in a single database or clinical center and did not further explore the factors that may affect the correlation between HB and DR. Differently, our study analyzed the HB-DR correlation in people from two different countries, performed subgroup analysis according to demographic characteristics, such as gender, age and BMI, and further explored the impact of DM duration on the correlation between HB levels and DR occurrence.
Cumulative evidence suggests that anemia is both a cause and consequence of DM-related complications [25, 26]. Several explanations have been proposed to illuminate the association between HB levels and DR. Compared with the normal population, due to the chronic inflammatory state of DM, the upregulation of various pro-inflammatory cytokines, such as interferon- γ, tumor necrosis factor α (TNF-α), and interleukin-6 (IL-6), can promote the occurrence of anemia in patients with DM. Specifically, the elevated level of pro-inflammatory cytokines such as IL-6 and TNF-α can reduce bone marrow sensitivity to erythropoietin (EPO), thus promoting anemia in patients with DR. Available studies suggest that the EPO resistance in chronic kidney disease may be attributed to the increased activity of T cells and monocytes, coupled by the production of pro-inflammatory cytokines in the bone marrow, which act locally and antagonize the action of EPO at the cellular level, thereby reducing the sensitivity of bone marrow to EPO [27]. Other studies demonstrate that chronic inflammation may upregulate hepcidin synthesis through the IL-6-STAT3 signaling pathway, which degrades intestinal ferroportin and inhibits iron absorption and macrophage iron release, leading to functional iron deficiency and affecting hemoglobin synthesis [28]. In addition, the disrupted hematological environment in patients with DM, such as extracellular hyperosmotic pressure, oxidative stress, and chronic hyperglycemia, can significantly reduce the survival duration of circulating red blood cell (RBC) in a direct manner [29]. Physiologically, non-enzymatic glycation reactions are widely present in organisms, which can produce irreversible advanced glycation end products (AGEs) through various complex non-enzymatic reactions. These products, in turn, can induce direct tissue damage and activate specific receptors (RAGEs), leading to DR [30, 31]. Available evidence also suggests that anemia-induced insufficient oxygen supply can result in the accumulation of AGE and activation of RAGE. One study documents that the rapid production of AGE in endothelial cells after hypoxia further activates RAGE-mediated signaling [32]. Another confirms that hypoxia induces the formation of AGE and activation of RAGE in macrophages [33]. Alternatively, other studies suggest that the thickening of the basement membrane and increased vascular permeability are two main retinal changes associated with the pathogenesis of DR [34, 35], in which hypoxic exposure can thicken the basement membrane by altering the deposition of collagen IV and laminin [36, 37]. In addition, retinal ischemia/hypoxia can also activate hypoxia inducible factor 1 (HIF-1) and upregulate the expression of vascular endothelial growth factor (VEGF) [38], with the latter increasing vascular permeability by phosphorylating the tight junction proteins such as occludin and zona occludin-1 (ZO-1) [39]. Still, low oxygen conditions can boost the production of reactive oxygen species (ROS), damaging the tissues inside and around retinal blood vessels and ultimately leading to DR [9]. However, given the complexities in DR occurrence and development, further studies are needed to reveal the specific mechanisms and interrelationships.
To further explore the correlation between HB levels and DR, we subsequently stratified the analysis by gender, age, and BMI in the MMC database. The analyses evidenced that irrespective of males and females, HB levels were significantly negatively correlated with DR occurrence in the age subgroups (40–49/50–59/60–69 years old) of the population, consistent with the previous findings in the NHANES database [40]. Our study also found that the significant negative HB-DR association was not affected by BMI stratification and persisted in different BMI subgroups. In the smooth curve fitting analysis of BMI stratification, we found a U-shaped association between HB levels and DR in the normal weight group at an inflection point of 12.7 (g/dL) for HB, indicating a negative HB-DR correlation when HB levels in the normal weight group are less than this threshold. The inflection point is similar to the HB value that defines anemia, which suggests a necessity of monitoring the hemoglobin level in this population and timely intervention to reduce the probability of DR.
In the MMC database, we further found that the HB-DR correlation may be influenced by the duration of DM and that HB levels were more closely related to DR in patients with a short duration (DM < 10 years). The possible explanation for this phenomenon may lie in that a prolonged duration of DM means a longer period of a hyperglycemic state, inducing an extended damage to the retinal microvascular endothelial cells and a sustained insensitivity to the changes in HB [41]. A previous study documents that the strength of the DM duration-DR association increases rapidly within a duration of 5–10 years [42]. These findings suggest that we should upgrade the management and follow-up of patients with a short DM duration and that an active intervention in HB levels should be considered to prevent the occurrence and development of DR. Future research is awaited to elucidate the potential mechanisms underlying the relations among HB, DM duration and DR.
Some limitations remain in the study. As it was a cross-sectional study, the causality between HB levels and DR remains unclear. To investigate the causal association, more stringent study designs are required, such as randomized controlled trials. In addition, despite adjustments for relevant potential covariates, other possible confounding factors may still exert influence. However, regardless of these limitations, this study expands our understanding of the association between HB and DR, and has valuable clinical significance.
Conclusions
In summary, the findings demonstrate a negative association between low HB levels and DR incidence in patients with T2DM, which can be impacted by the DM duration, and that HB levels are associated DR in a U-shaped manner in people with normal weight at an inflection point of 12.7 (g/dL). These findings highlight the importance of hemoglobin monitoring and HB intervention in the clinical management of patients with T2DM, especially those with a short DM duration.
Acknowledgements
We appreciate the platform that provided scientific support for this study. And we thank all patients who have allowed their identifying information to be used. In addition, we would like to thank all the professors and students involved in this study for their support of research ideas in the face of research problems.
Abbreviations
- DR
Diabetic retinopathy
- DM
Diabetes mellitus
- T2DM
Type 2 diabetes mellitus
- NHANES
National Health and Nutrition Examination Survey
- MMC
Metabolic Management Center
- BMI
Body mass index
- SBP
Systolic blood pressure
- DBP
Diastolic blood pressure
- FBG
Fasting blood glucose
- HbA1c
Glycosylated hemoglobin A1c
- HB
Hemoglobin
- ALB
Albumin
- TG
Triglyceride
- TC
Total cholesterol
- HDL
High-density lipoprotein-cholesterol
- LDL
Low-density lipoprotein-cholesterol
- hs-CRP
Hypersensitive C-reaction protein
- SUA
Serum uric acid
- BUN
Blood urea nitrogen
- Scr
Serum creatinine
- Egfr
Estimated glomerular filtration rate
- UACR
Urinary albumin/creatinine ratio
- CHD
Coronary heart disease
- OR
Odds ratio
- CI
Confidence interval
Author contributions
Kai-yi Mao: Writing-original draft, Resources, Methodology, Data curation, Visualization, Writing-review & editing. Rong-hua Liu: Writing-original draft, Methodology, Data curation, Visualization; Ping-ying Jiang: Visualization, Methodology, Investigation, Conceptualization; Wei-qi Cai: Investigation, Validation, Visualization; Yong-Xu Lin: Software, Validation, Visualization; Feng-Lin Chen: Writing-review & editing, Supervision, Investigation; Lin-Xi Wang: Writing-review & editing, Methodology, Investigation; Dan Li: Writing-review & editing, Validation, Supervision, Resources, Project administration, Conceptualization, Funding acquisition.
Funding
This study was funded by grants from Fujian Clinical Research Center for Digestive System Tumors and Upper Gastrointestinal Diseases [Grant number: 2022YGPT004]; National Key Clinical Specialty Construction Projects of Fujian Province, China [Grant number: 2023 − 1594]; Fujian Science and Technology Innovation Joint Fund Project, China [Grant number: 2019Y9062] and the Natural Science Foundation of Fujian Province [Grant number: 2020J011012].
Data availability
Data from the NHANES can be found on the website (https://www.cdc.gov/nchs/nhanes). The datasets of MMC used and analyzed during the current study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
Written informed consent was provided by the participants before the inclusion in the NHANES database, which was reviewed and permitted by the National Center for Health Statistics Ethics Review Committee. Data are anonymized to make them available for public use. The study protocol was reviewed by the Human Research Ethics Committee of Fujian Medical University Union Hospital and followed the Declaration of Helsinki (No. 2018KY072).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Kaiyi Mao and Ronghua Liu are co-first authors and contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data from the NHANES can be found on the website (https://www.cdc.gov/nchs/nhanes). The datasets of MMC used and analyzed during the current study are available from the corresponding author upon reasonable request.