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. 2025 May 28;9(7):107479. doi: 10.1016/j.cdnut.2025.107479

Low-Carbohydrate Diet Patterns That Favor High-Quality Carbohydrates Are Associated with Beneficial Long-Term Changes in Biomarkers of Inflammation and Oxidative Stress in the Framingham Offspring Cohort

Ghaida F Aloraini 1,2,3, Nicola M McKeown 2,4, Gail T Rogers 1, Sai Krupa Das 1,2, Alice H Lichtenstein 1,2, Paul F Jacques 1,2,⁎
PMCID: PMC12221638  PMID: 40612047

Abstract

Background

Low-carbohydrate diet (LCD) patterns have been studied as a potential strategy to reduce chronic inflammation. Evidence remains equivocal, likely because of differences in the quality of carbohydrate sources among LCD patterns.

Objectives

To prospectively examine the associations between LCD patterns varying in carbohydrate quality, and changes of inflammation and oxidative stress biomarkers.

Methods

Among Framingham Offspring Study participants (n = 2225; median 6.7-y follow-up, mean baseline age: 60.1 y), we used food frequency questionnaires to create 2 LCD scores (LCDS) accounting for quality of carbohydrates sources. These reflected higher intake of total fat and protein, and lower intake of either low-quality carbohydrates [high-quality LCDS (HQ-LCDS)] or high-quality carbohydrates [low-quality LCDS (LQ-LCDS)]. We calculated an inflammation and oxidative stress score by summing the standardized values of 9 inflammatory and oxidative stress biomarkers. Least-square mean change in the inflammation and oxidative stress score across quintile categories of LCDS was estimated using multivariable linear regression models, adjusted for potential confounders.

Results

HQ-LCDS was inversely associated with change in the inflammation and oxidative stress score (mean ± SE in Q1: 0.24 ± 0.16, Q5: −0.27 ± 0.16; Ptrend = 0.004), indicating that inflammation and oxidative stress increased in those in the lowest HQ-LCDS category and decreased in those in the highest category. HQ-LCDS was also inversely associated with changes in intercellular adhesion molecule-1 (Ptrend = 0.003) and lipoprotein phospholipase A2 activity (Ptrend = 0.003). No significant associations were observed for LQ-LCDS.

Conclusions

LCD patterns that preserved high-quality carbohydrates while replacing low-quality carbohydrates sources, such as refined grains and added sugars, with fat and protein were inversely associated with inflammation and oxidative stress score, potentially lowering chronic disease risk.

Keywords: low-carbohydrate diet, carbohydrate quality, chronic inflammation, oxidative stress, aging, Framingham Heart Study

Introduction

Chronic low-grade inflammation, characterized by elevated concentrations of inflammatory markers in the systemic circulation, plays a pivotal role in the development of many chronic diseases, including obesity [1], type 2 diabetes [2], cardiovascular disease (CVD) [3], neurodegenerative diseases [4], osteoarthritis [5], and some cancers [6], particularly in older adults. Low-carbohydrate diet (LCD) patterns have been studied as a potential strategy to modulate inflammation and reduce risk of metabolic dysfunction and chronic disease [[7], [8], [9], [10], [11], [12]].

One key component of LCD patterns that has not been adequately examined yet is the quality of carbohydrates remaining in the diet. Dietary carbohydrates come from many sources that can differentially affect chronic low-grade inflammation [[13], [14], [15]]. High-quality carbohydrate diets, characterized by higher intakes of whole grains, nonstarchy vegetables, whole fruits, nuts, and legumes, and correspondingly higher dietary fiber, are associated with lower circulating concentrations of inflammation biomarkers [[14], [15], [16], [17]]. Conversely, lower quality carbohydrate diets, characterized by higher intakes of refined grains, sugar-sweetened beverages, sweet-baked desserts, and other sweet snacks, promote chronic low-grade inflammation [[13], [14], [15], [16], [17], [18]].

Existing evidence from observational studies that examined the association between LCD patterns and biomarkers of inflammation has been primarily cross-sectional and limited to a few inflammatory biomarkers [11,12], whereas intervention studies have evaluated outcomes over follow-up periods ranging from a few weeks to 12 months [[19], [20], [21], [22], [23], [24], [25]]. Additionally, most of these studies did not assess the quality of dietary carbohydrates. Hence, the long-term association between LCD patterns while considering carbohydrate quality in these diets and changes in inflammation and oxidative stress biomarkers has yet to be adequately evaluated.

A common approach to capture exposure to LCDs in observational studies is to use low-carbohydrates diet scores (LCDSs), which simultaneously factor together lower carbohydrate and higher fat and/or protein intakes [[26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36]]. Several LCDSs have been created to capture variations in protein and fat sources [26,28,29,30,[32], [33], [34], [35], [36]]; however, only 2 studies have incorporated measures of carbohydrate quality into LCDSs [27,31]. These scores often combine macronutrients qualities into a single score, making it difficult to disentangle the independent effects of carbohydrate quality. For example, healthy LCDS (lower in low-quality carbohydrates, higher in plant protein and unsaturated fat) and unhealthy LCDS (lower in high-quality carbohydrates, and higher in animal protein and saturated fat) incorporate both carbohydrate quality and fat/protein sources to reflect an overall healthy diet and unhealthy diet patterns [27,31], which limits the ability to isolate the specific contribution of carbohydrate quality to health outcomes. To our knowledge, no previous study considered LCDSs that included carbohydrate quality independent of protein or fat quality or used LCDSs to examine the relationship between lower carbohydrate diets and biomarkers of inflammation and oxidative stress.

The objective of this study was to prospectively assess long-term associations between LCDSs representing lower intake of either high-quality carbohydrates or low-quality carbohydrates, in combination with higher total fat and protein, and changes in circulating and urinary biomarkers of inflammation and oxidative stress during a median 6.7-y follow-up period, among community-dwelling participants of the Framingham Heart Study (FHS) Offspring cohort. We hypothesized that an LCDS that represents maintenance of high-quality carbohydrates in a lower carbohydrate diet is associated with less inflammation and oxidative stress than a comparable LCDS representing a pattern with lower intake of high-quality carbohydrates.

Methods

Study population

This prospective study used data from the National Heart, Lung, and Blood Institute FHS Offspring cohort. The FHS is a long-term ongoing cohort study, initiated in 1948, to explore CVD risk factors in residents of Framingham, MA, United States [37]. In 1971, 5124 offspring of the FHS original cohort and their spouses were recruited into the Offspring cohort. Approximately every 4 y, this cohort undergoes a standard medical examination consisting of laboratory and anthropometric assessments, as well as dietary intake assessment. It is also continuously monitored for various cardiometabolic disorders, including CVD, diabetes, and hypertension [38]. Data from the seventh (1998–2001, n = 3539) and eighth (2005–2008, n = 3021) examination cycles were used for the analyses. Of the 3539 participants who attended examination 7, which is the baseline of our study, we excluded those who did not participate in follow-up examination 8 (n = 670), missing invalid dietary data (n = 275), missing inflammation and oxidative stress biomarkers measures (n = 304), and who were nonfasting (n = 65). Therefore, 2225 participants were included in the primary longitudinal analysis (Figure 1). All participants provided written informed consent before study participation. The Institutional Review Board for Human Research at Boston University Medical Center approved all the FHS data-collection protocols. This study protocol was approved by the Tufts Health Sciences Institutional Review Board.

FIGURE 1.

FIGURE 1

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Flowchart for selection of study participants.

Assessment of LCDS

The Harvard semiquantitative food frequency questionnaire (FFQ) assessed dietary intake at both examinations [39]. The FFQ was designed to capture the habitual dietary intake over the past 12 mo and included a list of 126 food items with standard serving sizes and 9 frequency categories ranging from “never, or <1 serving/mo” to “≥6 servings/d.” Invalid dietary data were defined as a total energy intake of <600 kcal/d for males and females, or ≥4000 kcal/d for females or ≥4200 kcal/d for males, or those with ≥12 blank items. Daily nutrient and energy intakes were computed by multiplying the frequency of intake of each food item by the nutrient and energy content and summing across all food items. The validity and reliability of the FFQ has been evaluated for both males [39] and females [40] in other cohorts. The correlation coefficients between FFQ and multiple diet records for females and males, respectively, are 0.61 and 0.73 for total carbohydrate, 0.52 and 0.44 for protein, 0.54 and 0.67 for fat. The validity of the FFQ was evaluated for dietary fiber in males with a correlation coefficient of 0.68 [39], and for crude fiber in females with a correlation coefficient of 0.56 [40].

To examine the carbohydrate quality in LCD patterns, we created 2 LCDSs. Our scores are modified versions of the original total LCDS (T-LCDS) developed by Halton et al. [26], which was created using estimates of total carbohydrate, total fat, and total protein intakes. To compute the score, the participants were divided into 11 approximately equal-sized categories each of fat, protein and carbohydrates intakes, expressed as a percentage of energy intake (%EI). For carbohydrates intake, participants in the lowest category were assigned a score of 10, whereas those in the highest category were assigned a score of 0. The scoring is the opposite for protein and fat, with those in the highest category of intakes assigned scores of 10 and those in the lowest category assigned scores of 0. The scores for each of the 3 macronutrients were summed for a total score. A higher T-LCDS reflects participants who had a lower energy intake from total carbohydrates and higher energy intakes from total fat and total protein, with a range of 0–30. Therefore, the higher the score, the more closely the participants follow an LCD pattern.

In our scores, we accounted for carbohydrate quality rather than scoring carbohydrate as a total, while maintaining the same total fat and protein scores for the HQ-LCDS and LQ-LCDS that were used in the original T-LCDS to limit confounding by fat and protein sources that replace the lower intake of high- or low-quality carbohydrate intake. To assess the carbohydrate quality of all carbohydrate-containing foods in the FFQ that contain ≥1 g of carbohydrates per serving, we applied a predetermined validated metric based on a 10:1 carbohydrate: fiber ratio with values ≤10 indicating high-quality carbohydrate source. This ratio is a close approximation of the carbohydrate: fiber ratio in whole-wheat flour and has been reported to effectively identify healthy whole grains, foods with more fiber, less sugars and sodium, and less likely to contain trans-fats, without energy increases [41,42]. By using this criterion, all whole fruits, all vegetables (except potatoes), whole grains (except for brown rice), and legumes and nuts were classified as high-quality carbohydrate foods. All refined grain products, and juices (except for tomato and grapefruit juice), sugar-sweetened beverages, dairy, beer, and wine were classified as low-quality carbohydrate foods.

We named our scores based on the type of carbohydrate source that was not limited in the scoring as this indicates the type of carbohydrate retained in the diet. In creating the high-quality LCDS (HQ-LCDS), the percent of energy intake from low-quality carbohydrates were scored. Participants with intakes in the lowest category of low-quality carbohydrates (3.9–23.2 %EI) were assigned a score of 10, whereas those in the highest category (42.4–61.8 %EI) were assigned a score of 0 (Supplemental Table 1). A higher HQ-LCDS reflects a diet pattern where participants consume less low-quality carbohydrates and more total fat and protein, with no constraints on the amount of high-quality carbohydrate foods consumed. For the low-quality LCDS (LQ-LCDS), high-quality carbohydrates were scored. Participants in the lowest intake category of high-quality carbohydrates (0.9–6.4 %EI) assigned a score of 10, whereas those in the highest category (23.8–43.2 %EI%) assigned a score of 0. A higher LQ-LCDS reflects a diet pattern where individuals consume less high-quality carbohydrates and more total fat and protein, with no constraints on the amount of low-quality carbohydrate foods consumed.

Inflammation and oxidative stress outcomes

We used 9 inflammation and oxidative stress biomarkers that were measured at the seventh and eighth examination cycles. Fasting blood samples were collected and stored at −80°C. Serum C-reactive protein (CRP) was measured with a highly sensitive assay. The following biomarkers were measured in duplicate by using commercially available enzyme-linked immunosorbent assay kits: serum interleukin-6 (IL-6), serum monocyte chemoattractant protein 1 (MCP-1), plasma P-selectin, plasma tumor necrosis factor receptor II (TNFRII), serum-soluble intercellular adhesion molecule 1 (ICAM-1), plasma osteoprotegerin (OPG), plasma lipoprotein phospholipase A2 (LPL-A2) mass and activity [43], and urine creatinine-corrected isoprostanes [44]. Values of the individual biomarkers were log transformed before statistical analysis, except for the creation of the inflammation and oxidative stress score, as described below. The antilog of the mean difference of the log transformed values represents a relative change (ratio) between examination 8 and examination 7 values. A value >1 indicates an increase in inflammation and oxidative stress, whereas a value <1 indicates a decrease in inflammation and oxidative stress.

The inflammation and oxidative stress score was created at each examination by first rank-normalizing the individual biomarker values, standardizing them as z-scores, and then summing them into a single score, adapted from a method previously described in the Framingham Offspring study [45]. The score was calculated as the sum of the standardized rank values of 9 biomarkers available at both examinations 7 and 8 (i.e., CRP, IL-6, MCP-1, P-selectin, TNFRII, ICAM-1, LPL-A2 mass, LPL-A2 activity, and OPG). Because of a high number of missing observations for urinary isoprostanes at examination 7, this biomarker was excluded from the score. Higher values of the score indicate higher inflammation and oxidative stress and a positive change in this score represents an increase in inflammation and oxidative stress.

We applied a Winsorization approach to outliers for the estimates of the change in outcomes by setting them to a value of 0.5 above the 95th percentile + (2 × IQR), or 0.5 below the fifth percentile − (2 × IQR).

Covariates

Several potential confounders of the relationship between LCD patterns and chronic inflammation were included as covariates in our analyses. These covariates were: age (years), sex (female, male), BMI (in kg/m2), menopausal status (menstruation ceased > 1 y), current smoking status (yes or no), physical activity [measured by a physical activity index (PAI) score based on the sum of sedentary, slight, moderate, and vigorous activity expressed as metabolic equivalent of task (h/d)] [46], alcohol intake (% energy/d), total energy intake (kcal/d), history of CVD (yes or no), nonsteroidal anti-inflammatory drug (NSAID) and corticosteroid use (any or none), and current hypertension, dyslipidemia, or diabetes or being on pharmacological treatment for any of these conditions (yes or no). To minimize missing data in covariates, we imputed missing values for the following covariates: height, BMI, and PAI. For height, missing values were imputed by bringing forward values from the previous examination, if previous examination value was not available, we brought backward values from the following examination. Missing BMI data were calculated by using the available weight and imputed height data. PAI missing data were imputed by determining the median PAI score of the sample who had available PAI data, stratified by age, sex, BMI, and perceived health status, and applying these medians to individuals with missing PAI data.

Statistical analyses

The LCDSs were averaged across examinations 7 and 8 to account for long-term intake and divided into quintile categories. Our primary outcome was the change in inflammation and oxidative stress score. Secondary outcomes included changes in the logged values of the individual biomarkers. Change in each outcome was calculated as the value at examination 8 minus the value at examination 7. Our primary analysis estimated associations between HQ-LCDS, and LQ-LCDS, and the change in the inflammation and oxidative stress score. Secondary analyses included: 1) assessing associations between HQ-LCDS, and LQ-LCDS against changes in logged values of the individual biomarkers, and 2) assessing T-LCDS against changes in the inflammation and oxidative stress score and logged values of the individual biomarkers.

We used multiple linear regression models (SAS PROC GLM) to estimate least-square means of the change in the outcome in each quintile category of LCDS, adjusted for age, sex, alcohol intake, energy intake, smoking status, BMI, PAI, menopausal status, and the baseline (examination 7) value of the outcome (model 1); plus NSAID and corticosteroid use, history of CVD, and current hypertension, dyslipidemia, or diabetes, or being on pharmacological treatment for any of these conditions (model 2). P-trend across quintile categories of LCDSs was assessed by assigning the median value in each quintile category and treating it as a continuous variable in regression models. For the individual biomarkers, the logged values of least-square means were then back transformed by exponentiating them, and we applied approximate back transformation using delta method for the standard errors by multiplying the exponentiated least-square means by the standard errors of the means in the log scale [47].

We conducted several sensitivity analyses to ensure the robustness of the associations. First, we excluded observations with studentized residuals beyond ±2. However, as these findings were consistent with the results that included these observations, we did not present this analysis. Second, we excluded participants who are on pharmacological treatment for hyperglycemia because these medications can impact markers of inflammation by improving blood glucose concentration. Furthermore, we adjusted models for waist circumference instead of BMI, because of the known associations between waist circumference and inflammatory markers.

All statistical analyses were performed using SAS (version 9.4; SAS institute). All reported P values are 2-sided and statistical significance was set at a nominal α level of 0.05.

Results

The median follow-up time was 6.7 y (IQR: 0.7 y; minimum: 3.7 y; maximum: 8.6 y). At baseline, the median (25th to 75th percentile) age of participants was 59 y (53y–67y), 56% were female, and the mean BMI was 27.3 (Table 1). For both LCD scores, the highest quintiles included more females, more tobacco users, more individuals with diabetes, and participants exhibited higher BMI. Across increasing quintile categories of LQ-LCDS, participants had poorer diet quality as indicated by lower Dietary Guidelines for Americans Adherence Index (DGAI) score [48], whereas across increasing quintile categories of HQ-LCDS, participants had similar DGAI scores.

TABLE 1.

Baseline characteristics by quintiles of HQ-LCDS and LQ-LCDS in 2225 participants of the Framingham Offspring Study at examination 71.

Total HQ-LCDS
 LQ-LCDS
Quintile 1 Quintile 5 Quintile 1 Quintile 5
Median score (IQR)2 – 6 (4––7) 24 (23––27) 7 (5––8) 23 (22––25)
Characteristics3
n 2225 417 437 406 437
Age (y) 59 (53–67) 60 (53–67) 60 (54–67) 62 (56–68) 57 (52–65)
Female, n (%) 1232 (55.4) 204 (48.9) 275 (62.9) 217 (53.4) 239 (54.7)
BMI (kg/m2) 27.3 (24.4–30.5) 26.5 (23.6–29.2) 27.8 (24.8–31.7) 26.3 (23.6–29) 28.3 (25.2–32.2)
PAI, MET (h/d) 36.8 (34.1–40) 36.4 (33.8–39.3) 36.8 (33.8–40.2) 36.8 (34.1–40.2) 36.9 (34.4–40.2)
Current smoker, n (%) 246 (11.1) 43 (10.2) 54 (12.3) 33 (8.1) 64 (14.7)
NSAID and corticosteroid use, n (%) 604 (27.1) 114 (27.4) 123 (28.2) 101 (24.8) 121 (27.7)
Hypertension, n (%) 925 (41.6) 170 (40.7) 177 (40.5) 174 (42.8) 197 (45.1)
Dyslipidemia, n (%) 1191 (53.5) 242 (58.0) 216 (49.5) 222 (54.6) 228 (52.1)
Diabetes, n (%) 219 (9.8) 15 (3.7) 80 (18.3) 24 (6.0) 54 (12.4)
History of CVD, n (%) 228 (10.2) 55 (13.1) 51 (11.7) 52 (12.8) 51 (11.7)
Dietary intake4
DGAI score 61.6 (52.7–69.6) 60.0 (52.5–67.3) 58.9 (51.3–67.3) 68.2 (62.6–74.7) 51.9 (45.1–58.6)
Total energy (kcal) 1764 (1403–2173) 1852 (1475–2230) 1633 (1343–2026) 1719 (1396–2101) 1716 (1351–2160)
Total carbohydrate (%EI) 50.1 (44.4–55.5) 58.8 (54.5–63.5) 41.6 (37.6–45.4) 60.2 (56.6–64) 41.7 (37.8–44.6)
Total fat (%EI) 30.7 (26.2–35) 24.7 (21.4–28.2) 36.8 (33.5–39.6) 23.2 (20.9–26.2) 37.3 (34.7–40.6)
Total protein (%EI) 17.2 (15.2–19.4) 14.3 (12.7–15.8) 20.5 (18.7–22.4) 15.6 (14.0–17.0) 19.6 (17.5–21.7)
Alcohol (%EI) 1.6 (0–5.4) 2.1 (0–6.9) 0.9 (0–4.3) 1.6 (0–5.7) 1.1 (0–4.1)
HQ-carbohydrate (%EI) 13 (8.7–18.7) 11.5 (7.5–17.9) 13.1 (9.1–17.9) 21.5 (16.9–26.7) 8.1 (6.1–10.9)
LQ-carbohydrate (%EI) 34.1 (29.1–39.3) 43.4 (39.6–48.5) 26.4 (22.6–29.3) 36.4 (30.9–41.1) 31.4 (27.0–35.7)
Total fiber (g) 17.3 (13.1–22.4) 17.2 (12.9–22.7) 15.6 (11.8–20.7) 21.9 (16.3–27.0) 13.2 (10.1–17.6)
SFA (%EI) 10.6 (8.8–12.5) 8.6 (7.1–9.8) 12.8 (11.1–14.4) 7.7 (6.6–8.9) 13.2 (11.9–14.9)
PUFA (%EI) 11.2 (9.4–13) 9.0 (7.5–10.5) 13.6 (12.2–15.1) 8.3 (7.2–9.5) 13.9 (12.8–15.4)
MUFA (%EI) 5.6 (4.7–6.7) 4.7 (4–5.6) 6.7 (5.7–7.7) 4.8 (4.0–5.6) 6.4 (5.5–7.6)
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Abbreviations: %EI, percent of total daily energy; CVD, cardiovascular disease; DGAI, Dietary Guidelines Adherence Index; HQ-LCDS, high-quality low-carbohydrate diet pattern score; LCDS, low-carbohydrate diet pattern scores; LQ-LCDS, low-quality low-carbohydrate diet pattern score; MET, metabolic equivalent task; MUFA, monounsaturated fatty acids; NSAID, nonsteroidal anti-inflammatory drug; PAI, physical activity index; PUFA, polyunsaturated fatty acids; SFA, saturated fatty acids.

1

Values are medians (and IQRs: 25th–75th percentile) or frequency (and percentages).

2

Adjusted for age, sex and total energy.

3

Adjusted for age and sex.

4

All dietary intake variables were adjusted for age, sex, and total energy, except total energy variable, which was adjusted for age and sex only.

The median dietary intakes, expressed as a percentage of energy, were 50% for total carbohydrate, with 13% from high-quality carbohydrates sources and 34% from low-quality carbohydrate sources. Total carbohydrate intake was lower by ∼19% in the highest compared with the lowest quintile categories of both LCDSs. Low-quality carbohydrates were lower by 17% in the highest compared with the lowest quintile category of HQ-LCDS, whereas high-quality carbohydrates were lower by 13% in the highest compared with the lowest quintile category of LQ-LCDS. Baseline concentrations of inflammation biomarker by HQ-LCDS and LQ-LCDS quintile categories are presented in Supplemental Table 2.

HQ-LCDS was inversely associated with the change in the inflammation and oxidative stress score over 6.7-y follow-up period (mean ± SE in Q1: 0.24 ± 0.16; Q5: –0.27 ± 0.16; Ptrend = 0.004), after adjusting for model 1 covariates (Table 2). The results indicate that inflammation and oxidative stress increased in those with the lowest HQ-LCDS, whereas it decreased in those with the highest HQ-LCDS. This association remained significant in the fully adjusted model. In secondary analyses, we observed a significant association between the T- LCDS and inflammation and oxidative stress score (Q1: 0.11 ± 0.16; Q5: –0.28 ± 0.16; Ptrend = 0.04) in model 1, which was similar to, although more modest than, that observed for HQ-LCDS. This association remained significant in the fully adjusted model (Table 2). The secondary analyses examining the associations between HQ-LCDS and changes in individual biomarkers indicated that HQ-LCDS was inversely associations with ICAM-1 (Q1: 1.20 ± 0.02; Q5: 1.14 ± 0.02; Ptrend = 0.003) and LPL-A2 activity (Q1: 1.01 ± 0.01; Q5: 0.97 ± 0.01; Ptrend = 0.001), but no significant association was observed with the other markers of inflammation and oxidative stress (Supplemental Table 3). We also found statistically significant inverse associations between T-LCDS and ICAM-1 (Q1: 1.20 ± 0.02; Q5: 1.15 ± 0.02; Ptrend = 0.03), and LPL-A2 activity (Q1: 0.99 ± 0.01; Q5: 0.97 ± 0.01; Ptrend = 0.04) (Supplemental Table 4).

TABLE 2.

Adjusted least-square means of change in inflammation and oxidative stress score across quintiles of LCDSs in the Framingham Offspring Study1.

LCDS Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 P-trend2
HQ-LCDS, n 438 444 468 412 463
Median (range) 7.5 (0–9.5) 11.5 (10–13) 15 (13.5–16.5) 18.5 (17–20) 23 (20.5–30)
Model 13 0.24 ± 0.16 0.35 ± 0.16 –0.19 ± 0.15 –0.11 ± 0.16 –0.27 ± 0.16 0.004
Model 24 0.28 ± 0.16 0.36 ± 0.16 –0.18 ± 0.15 –0.12 ± 0.16 –0.31 ± 0.16 0.001
LQ-LCDS, n 464 391 471 462 437
Median (range) 8.5 (1–10.5) 12 (11–13) 15 (13.5–16) 18 (16.5–19.5) 22 (20–30)
Model 1 0.00 ± 0.16 –0.16 ± 0.17 0.01 ± 0.15 0.19 ± 0.15 –0.07 ± 0.16 0.73
Model 2 0.03 ± 0.16 –0.14 ± 0.17 –0.01 ± 0.15 0.19 ± 0.15 –0.09 ± 0.16 0.95
T-LCDS, n 459 424 474 416 452
Median (range) 7 (0–9.5) 11.5 (10–13) 15 (13.5–16.5) 18.5 (17–20.5) 23.5 (21–30)
Model 1 0.11 ± 0.16 0.22 ± 0.16 0.00 ± 0.15 –0.03 ± 0.16 –0.28 ± 0.16 0.04
Model 2 0.14 ± 0.16 0.23 ± 0.16 0.00 ± 0.15 –0.04 ± 0.16 –0.32 ± 0.16 0.02
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Abbreviations: HQ-LCDS, high-quality low-carbohydrate diet pattern score; LQ-LCDS, low-quality low-carbohydrate diet pattern score; NSAID, nonsteroidal anti-inflammatory drug; T-LCDS, total low-carbohydrate diet pattern score.

1

Values are least-square adjusted means ± SE.

2

P < 0.05 considered significant.

3

Model 1 adjusted for age (continuous), sex (male/ female), BMI (continuous), total energy (continuous), alcohol intake (%EI/d), physical activity index (continuous), current smoker (yes no), menopausal status (yes/no periods had stopped for ≥1 y), and baseline inflammation score value (continuous).

4

Model 2 adjusted for model 1 covariates + history of cardiovascular disease (yes or no), NSAID and corticosteroid use (yes or no), current hypertension, dyslipidemia, or diabetes, or on pharmacological treatment for these conditions (yes or no).

No significant associations were identified between LQ-LCDS and change in the inflammation and oxidative stress score (Table 2). In a secondary analysis, statistically significant positive associations were observed between LQ-LCDS and the change in MCP-1 (Q1: 1.20 ± 0.01; Q5: 1.21 ± 0.01; Ptrend = 0.04), and LPL-A2 mass (Q1: 0.67 ± 0.01; Q5: 0.69 ± 0.01; Ptrend = 0.04) in the fully adjusted model, suggesting that higher adherence to LQ-LCDS is associated with higher inflammation (Supplemental Table 5).

After excluding participants on pharmacological treatment for hyperglycemia in a sensitivity analysis, there was a modest attenuation in the inverse associations between HQ-LCDS and the inflammation and oxidative stress score (Supplemental Table 6), and ICAM-1 and LPLA2 activity (Supplemental Table 7), although these associations remained significant. However, the inverse associations between T-LCDS and the inflammation and oxidative stress score (Supplemental Table 6), and ICAM-1 and LPLA2 activity (Supplemental Table 8) substantially attenuated to nonstatistically significant. No association was observed between LQ-LCDS and the inflammation and oxidative stress score (Supplemental Table 6), and the change in MCP-1, but not LPL-A2 mass, was attenuated (Supplemental Table 9). Although there was ∼10% reduction in sample size after excluding participants on pharmacological treatment for hyperglycemia, it was not the reason for the attenuation. In another sensitivity analysis, when adjusting models for waist circumference instead of BMI, we observed modest changes in the associations between LCDSs with inflammation and oxidative stress score and individual biomarkers (Supplemental Tables 10–13).

Discussion

In this prospective longitudinal study, we found that an LCD pattern that retained high-quality carbohydrates was associated with favorable changes in the inflammation and oxidative stress score. Specifically, we observed the strongest inverse association between the HQ-LCDS, for which a higher score represented a lower intake of low-quality carbohydrates, and changes in inflammation and oxidative stress, whereas we observed no significant association between the LQ-LCDS, for which a higher score represents a lower intake of high-quality carbohydrates, and changes in inflammation and oxidative stress in spite of the fact that total carbohydrate intake was similar among those with the highest HQ-LCDS and LQ-LCDS. The similar association observed for T-LCDS and HQ-LCDS is likely because of the similar intakes of low-quality carbohydrates among those who scored high on both scores, a consequence of the fact that the majority of carbohydrates consumed by participant in this cohort were from low-quality carbohydrate sources.

Overall, our findings suggest that among those who are consuming moderately low-carbohydrate diets (i.e., carbohydrate intake in the lowest quintile category of all LCDSs was ∼41 EI%), eating fewer low-quality carbohydrates is associated with less inflammation and oxidative stress, which are consistent with recommendation to limit intake of added sugars and refined grains, and increase intake of whole grains, fruits, vegetables, and legumes. These findings are important considering the increasing popularity of LCDs and the large number of people adopting these diets for perceived health benefits [49].

The favorable associations between lower intakes of carbohydrates from low-quality food sources extend the previous evidence on the proinflammatory effects of low-quality carbohydrates. There is evidence that added sugars available in refined carbohydrates, sugar-sweetened beverages, sweet snacks, and dairy desserts may negatively impact inflammatory status [50,51]. Sugar-sweetened beverages [52], major contributors to added sugar intake, have been associated with higher concentrations of inflammatory biomarkers in cross-sectional, observational studies [[53], [54], [55], [56], [57], [58], [59], [60]] and a few randomized controlled clinical trials [[61], [62], [63], [64]].

Several possible mechanisms could be involved in the association between low-quality carbohydrates food sources with chronic inflammation. First, low-quality carbohydrates have a high glycemic index that results in higher and more rapid increase in blood glucose concentration [65]. In vitro, studies have shown that glucose can cause oxidation of membrane lipids, proteins, lipoproteins, and DNA and activate inflammation [65]. Additionally, lower quality carbohydrates food sources are low in dietary fiber, which helps to maintain healthy digestive system and play an important role in modulating inflammation [15]. In fact, it has been suggested that a diet rich in fiber can decrease the systemic inflammatory response by improving the intestinal barrier function and modulating the intestinal microbiota [15]. This is attributed to the production of short-chain fatty acids by the gut microbiota, which is dependent on adequate dietary fiber. Short-chain fatty acids are thought to play a key role in neuroimmune-endocrine regulation [15]. Moreover, the refining process of carbohydrates removes various essential nutrients, such as B vitamins, vitamin E, and minerals like iron, potassium, magnesium, and zinc, present in whole grains. These nutrients exert anti-inflammatory effects and are involved in supporting the immune response [66]. Although most refined grains products are enriched, which means certain nutrients (thiamin, riboflavin, niacin, folic acid, and iron) are added back after processing, they are still lacking in fiber, magnesium, zinc, potassium, and vitamins E and B6 [67]. Furthermore, high consumption of added sugars can represent a substantial source of endogenous advanced glycation end products. Advanced glycation end products are compounds formed when sugars react with proteins or fats in a process known as glycation. These compounds have been linked to increased inflammation and oxidative stress [68].

We found similar beneficial associations between HQ-LCDS and T-LCDS with changes in inflammation and oxidative stress. This is likely because most of carbohydrate intake in our cohort is from low-quality carbohydrate sources, which is similar to the findings from a recent nationally representative data from National Health and Nutrition Examination Survey, where mean energy intake from total carbohydrate was 51%, with 42% from low-quality carbohydrates sources and 9% from high-quality carbohydrate sources [69]. However, the associations between LCDS and favorable changes in inflammation and oxidative stress score are attenuated in T-LCDS compared with HQ-LCDS, partially because for those in the highest quintile category of T-LCDS both high-quality and low-quality carbohydrates intakes were lower.

The favorable associations found between HQ-LCDS and T-LCDS with the change in inflammation/oxidation score could be also because of higher dietary protein intake. In a previous study using data from Framingham Offspring cohort, higher dietary protein intake, particularly from plant sources, was associated with favorable changes in inflammation/oxidative stress as assessed by the overall inflammation and oxidative stress score [70]. However, the protein distribution in all of the LCD scores was similar, so this is unlikely to be responsible for any observed beneficial association as no association was observed with the LQ-LCDS.

Likewise, the higher LCDSs also reflected higher intake of total fats, including higher polyunsaturated fatty acids, in our population. Omega-3 fatty acids have been associated with lower concentrations of inflammatory biomarkers, including CRP, IL-1 and IL-6, prostaglandin, and cytokines, in many observational and intervention studies [[71], [72], [73], [74], [75], [76], [77], [78], [79], [80]]. The role of omega-6 fatty acids and their interactions with omega-3 fatty acids in modulating the inflammatory process is complex and still not properly understood [81]. Because the increase in different fatty acids was identical in all scores, as with protein, it is unlikely that the increase in fatty acids is responsible for the beneficial association with inflammation and oxidative stress biomarkers, as no association was observed with the LQ-LCDS.

A major strength of this study is the large, well-characterized cohort followed for a median of 6.7 y with repeated measures of exposures and outcomes from which changes in circulating and urinary biomarkers of inflammation and oxidative stress could be derived. Additionally, in our analyses, we used a combination of 9 inflammation and oxidative stress biomarkers that are commonly used in observational and intervention studies; they are widely linked to chronic diseases and their risk factors, as well as aging process; and diet has been shown to be associated with their concentrations [13,14,82,83]. Moreover, our study focused on the impact of carbohydrate quality in lower carbohydrate diet patterns and changes in inflammation and oxidative stress biomarkers. Although no previous studies have specifically examined LCD scores in relation to inflammation and oxidative stress, some have explored associations with cardiometabolic risk factors by simultaneously considering fat quality, protein sources, and carbohydrate quality. However, these studies did not isolate the independent contribution of carbohydrate quality. In contrast, our study was designed to focus on the role of carbohydrate quality in lower carbohydrate diets and minimize confounding by developing LCD scores that disentangle carbohydrate quality from fat and protein sources.

Our study had some limitations. First, the use of self-reported FFQs to assess participants’ dietary intake may be limited by recall and social desirability biases that lead to potential misclassification of nutrient intake. However, FFQs provide good estimates of relative intake and are appropriate for ranking individuals’ intakes. Second, we could not examine the associations with very low-carbohydrate diet patterns as few individuals in our study population consumed <25% of energy from carbohydrates. Third, we did not adjust for overall diet quality in our models because of the high correlation between the quality of carbohydrates and diet quality, which may lead to over adjustment bias. Fourth, although we addressed confounding in several ways, residual confounding by unmeasured dietary or other lifestyle factors may also affect our results. Finally, the generalizability of our conclusions may be limited, as the Framingham Offspring cohort is predominantly Caucasian American males and females.

The findings of our study suggested that long-term adherence to LCD patterns, particularly one that includes a lower intake of low-quality carbohydrate but maintains high-quality carbohydrates, is associated with lower inflammation and oxidative stress score, which may result in a reduced chronic diseases risk in the aging populations. This lends support to recommendations on preserving high-quality carbohydrates in the context of lower carbohydrate diet pattern and replacing low-quality carbohydrate foods with healthy sources of fat and protein.

Author contributions

The authors’ responsibilities were as follows – GFA, GTR: conducted research and performed statistical analysis; GFA: drafted the article; GTR, NMM, AHL, SKD, PFJ: made major contributions; PFJ: had primary responsibility for final content; and all authors: have read and approved the final manuscript.

Data availability

Data described in the manuscript, code book, and analytic code will be made available upon request pending application to and approval by the Framingham Heart Study.

Funding

The study was supported by the Institute for the Advancement of Food and Nutrition Sciences, the Saudi Arabia Ministry of Education, and with Federal funds from the U.S. Department of Agriculture (USDA; agreement no. 58-8050-9-004) and the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under contract no. 75N92019D00031. The funders had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript.

Conflict of interest

PFJ is a member of the Danone North America Essential Dairy and Plant-Based Advisory Board. NMM is an unpaid scientific advisor for the Whole Grains Council.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.cdnut.2025.107479.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

multimedia component 1
mmc1.docx (306.1KB, docx)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

multimedia component 1
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Data Availability Statement

Data described in the manuscript, code book, and analytic code will be made available upon request pending application to and approval by the Framingham Heart Study.

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