Obesity affects 890 million adults worldwide(1) and is a major risk factor for cardiometabolic disease(Reference Valenzuela, Carrera-Bastos and Castillo-García2), contributing to more than 1·6 million premature deaths each year globally(3). By 2035, the global economic burden attributable to obesity is projected to exceed $4 trillion annually (USD)(4). As suboptimal diet is a modifiable risk factor for obesity(Reference Afshin, Sur and Fay5), identifying determinants of adiposity is an urgent public health priority.
Linoleic acid (LA), an essential n-6 PUFA, accounts for 85–90 % of n-6 PUFA intake in the Western diet and is abundantly sourced from seed oils (e.g. soyabean, corn, cottonseed oils), nuts and seeds(Reference Harris, Mozaffarian and Rimm6,Reference Innes and Calder7) . The majority of non-LA n-6 PUFA in the diet and the blood consist of arachidonic acid (AA)(Reference Harris, Mozaffarian and Rimm6). Others such as dihomo-gamma-linolenic acid, γ-linolenic acid, adrenic acid and Osbond acid are predominantly determined by metabolic processes, though AA is minimally available in poultry, eggs and meat sources(Reference Innes and Calder7). Although LA is well studied for its cardiometabolic effects(Reference Jackson, Harris and Belury8), its role in body-weight regulation remains contested(Reference Belury9).
Several arguments have been raised that point to potential adverse effects of all n-6 PUFA, or LA in particular. First, ecological observations show that rising n-6 PUFA levels in the diet (and a parallel decline in saturated fats) coincided with increased cardiometabolic disease rates in the early 20th century(Reference Simopoulos10). Second, LA serves as a precursor to AA, which is a substrate for several pro-inflammatory eicosanoids(Reference DiNicolantonio and O’Keefe11), fuelling speculation that a high LA intake may promote chronic systemic inflammation and obesity via its conversion to AA(Reference Innes and Calder7). More recently, all n-6 PUFA (including LA) attracted adverse publicity related to their association with industrial food processing(Reference Crouch12,13) . In terms of mechanistic concerns, studies in animal models (C57BL/6j mice) demonstrated that elevated levels of dietary LA increased endocannabinoid production, promoted weight gain and impaired insulin signalling(Reference Alvheim, Torstensen and Lin14,Reference Mamounis, Yasrebi and Roepke15) . Specifically, a diet comprised of 22·5 % energy intake from LA induced greater weight gain than saturated fat diets, despite the absence of hypothalamic inflammation(Reference Mamounis, Yasrebi and Roepke15). However, this dose far exceeds typical human intakes, and the applicability of these findings to humans remains questionable.
In contrast, clinical interventions generally report reduced systemic inflammation and improved body composition with the addition of LA to the diet(Reference Belury9,Reference Summers, Fielding and Bradshaw16–Reference Norris, Collene and Asp19) , especially in lean mass(Reference Rosqvist, Iggman and Kullberg17,Reference Norris, Collene and Asp19) . Observational cohorts also link higher levels of circulating total n-6 PUFA, LA and, in some cases, AA, to anti-inflammatory profiles(Reference Lai, Ryder and Tintle20–Reference Crick, Halligan and Davey Smith22). In studies incorporating anthropometric measures, circulating LA was positively associated with lean tissue volume(Reference Belury, Cole and Bailey23) and skeletal muscle mass(Reference Su, Zhang and Hu24), inversely associated with trunk adipose mass(Reference Belury, Cole and Bailey23) and abdominal obesity(Reference Alsharari, Riserus and Leander25), although associations for weight gain and BMI are mixed(Reference Belury, Cole and Bailey23,Reference Hastert, de Oliveira Otto and Lê-Scherban26,Reference Wang, Manson and Rautiainen27) . Findings are less consistent for the associations between AA and BMI(Reference Hastert, de Oliveira Otto and Lê-Scherban26,Reference Chen, Ao and Lan28) , while other non-LA n-6 PUFA have not been individually studied. LA was also inversely associated with metabolic syndrome(Reference Vanhala, Saltevo and Soininen29,Reference Lee, Tai and Sim30) , suggesting that at population-relevant exposures, higher LA is unlikely to promote and might even attenuate weight gain.
Despite this body of work, some clarifications are warranted. Findings from studies that rely on dietary self-report methods are less consistent, reporting largely null associations between n-6 overall or LA in particular with anthropometric measurements(Reference Chen, Ao and Lan28,Reference Karami, Hadi and Mohit31–Reference Jakobsen, Madsen and Dethlefsen34) , potentially reflecting exposure misclassification. Furthermore, few studies have incorporated repeated measures to assess baseline levels and longitudinal changes over time. Large-scale studies using objective biomarkers of LA status, particularly in relation to adiposity outcomes, are also sparse. To address these limitations from prior studies, we examined the cross-sectional and longitudinal relationship between circulating LA levels in relation to body weight and adiposity outcomes in the UK Biobank (UKBB), a large, prospective cohort.
Methods
Study population
The UKBB is a prospective, population-based cohort of ∼500 000 individuals recruited between 2007 and 2010 at assessment centres across England, Wales and Scotland. Baseline data were collected using questionnaires, biological samples and physical measurements. Ongoing longitudinal monitoring occurs via a mix of in-person measurements, in-person and online questionnaires, as well as nearly real-time electronic medical record and death registry integration(Reference Collins35,Reference Sudlow, Gallacher and Allen36) . To be included, participants needed to have data on fatty acid and all covariates used in the analysis. Of 502 128 subjects in the UKBB, FA data were available on a random sample of 274 003. After removing 1416 individuals with incomplete information on covariates (anthropometric measures, n 1090; Townsend Deprivation Index(Reference Townsend, Phillimore and Beattie37), n 326), the final sample available for cross-sectional analysis was 272 587 (online Supplementary Figure 1). For longitudinal associations, we used the subsample of 98 927 individuals who attended at least one follow-up and used the first instance of those who attended multiple follow-ups. Of these 98 927 individuals, a random sample of 59 394 had baseline fatty acids. After removing an additional 1059 individuals for missing covariate data, our final sample of 58 335 individuals included follow-up measures from the repeat assessment (n 17 906), the imaging visit (n 40 192) or the first repeat imaging visit (n 237)(Reference Littlejohns, Holliday and Gibson38) (online Supplementary Figure 1).
Exposure
Our two primary exposures are plasma LA and non-LA n-6 (each expressed as a percentage of total plasma FA). The secondary exposure is total n-6. The level of total n-6 and LA was determined on baseline plasma samples using NMR (Nightingale Health)(Reference Würtz, Raiko and Magnussen39); analytical performance characteristics have been reported(Reference Julkunen, Cichońska and Tiainen40). Non-LA n-6 is computed as the difference between total n-6 and LA.
Outcomes
Our primary outcomes are waist circumference, weight (both of which are measured rather than estimated) and whole-body fat mass (as it is the dominant compartment among other estimated outcomes). Secondary outcomes included BMI, whole-body fat-free mass, trunk fat mass and trunk fat percent. For cross-sectional associations, we relied on anthropometric measurements taken at baseline (2006–2010). For analyses that explored changes over time, we relied on follow-up measurements taken in 2012–2013 during the first repeat assessment, 2014 and onwards for an imaging visit and 2019 and onwards for the first repeat imaging visit. Waist circumference was taken by a Seca 200 cm tape measure, standing height by Seca 240 cm height measure, and the remaining measurements were collected by a Tanita BC418MA body composition analyser (https://tanita.eu/understanding-your-measurements), all via standard protocol (https://biobank.ctsu.ox.ac.UK/ukb/ukb/docs/Anthropometry.pdf).
Covariates
Pre-planned covariates were considered based on biological interest, current or previously observed associations with n-6 or adiposity outcomes and meaningful changes in the exposure risk estimate (±5 %). Information on all reported sociodemographic characteristics, diet and lifestyle factors was collected via a touchscreen questionnaire. The electronic questionnaire and other resources can be found on the UKBB website (https://www.ukbiobank.ac.UK/resources/). In brief, pre-planned covariates included in our models were age, sex, education, Townsend Deprivation Index, ethnicity, physical activity and levels of DHA. The inclusion of DHA in the model allows us to assess the association between n-6 PUFA and adiposity independent of the influence of DHA, as n-3 PUFA share metabolic pathways with n-6 PUFA. Details of covariates used and their corresponding ID in the UKBB database can be found in online Supplementary Table 1.
Statistical analysis
Sample characteristics were summarised using standard approaches (mean/sd; n/%). The associations between the exposures of interest (LA and non-LA n-6 PUFA) and cross-sectional outcome measures were assessed with multivariable linear models, which adjusted for relevant covariates. Longitudinal linear models, with findings reported as standardised betas (95 % CI), predicted changes in outcome measures divided by the length of time (years) between measurements to account for person-to-person differences in exposure time and adjusting for relevant covariates. Associations are expressed as a change in the outcome measure per interquintile range (IQ5R, defined as 90th minus 10th percentiles of each exposure of interest) or per quintile (Q; relative to the lowest quintile (Q1)) and via a linear trend across fatty acid quintiles. For longitudinal models, additional adjustments for baseline weight, baseline waist circumference and baseline whole-body fat mass were included. Exploratory analyses used restricted cubic splines to test for potential non-linearity v. linear models between LA and the three primary cross-sectional and three primary longitudinal outcomes. Interaction terms between continuous LA (IQ5R) and sex or (separately) age decade (40–50, 50–60, 60–70) were added to each model (cross-sectional and longitudinal) to test for potential sex or age modification of the LA–outcome relationships. Additionally, for the relationship between LA and whole-body fat mass and whole-body fat-free mass, mutual adjustments were included as a sensitivity analysis to control for potential confounding. A significance level of 0·05 was used for all analyses.
Results
Participant characteristics
We evaluated a total of 272 587 participants for the cross-sectional analysis and 58 335 participants for the longitudinal analysis (Table 1). Distributions of sex, ethnicity, education, Townsend Deprivation Indices and physical activity were similar between the cross-sectional and longitudinal sub-cohorts. About half were female, nearly all were White and just about half reported a college education. Participants also tended to be slightly overweight at baseline, with a general reduction in weight and whole-body fat-free mass during follow-up. Correlations between anthropometric measures are reported in online Supplementary Table 2.
Participant characteristics of the UK Biobank
Table 1. Long description
The table presents participant characteristics of the UK Biobank, comparing cross-sectional and longitudinal analyses. It includes variables such as sex, age, ethnicity, education, Townsend Deprivation Indices, physical activity, fatty acid measurements, and anthropometric measures. The table has 51 rows and 5 columns, with headers for Frequency, Percentage, and Mean with Standard Deviation. Key trends include similar distributions of sex, ethnicity, education, and physical activity between the two sub-cohorts. About half of the participants are female, nearly all are White, and just about half reported a college education. Participants tend to be slightly overweight at baseline, with a general reduction in weight and whole-body fat-free mass during follow-up. Notable data points include mean ages of 56.6 years for cross-sectional and 55.4 years for longitudinal participants, and mean body mass indices of 27.5 and 26.8 kilograms per square meter, respectively.
* Paired t tests were conducted between baseline and repeated measures: all were statistically significantly different (P < 0·001), except for BMI (P = 0·135).
Cross-sectional relationship between levels of n-6 and primary measures
In multivariable models, LA was inversely associated with waist circumference, weight and whole-body fat mass (Figure 1). In comparison with the lowest quintile, participants in the highest quintile had a statistically significant smaller waist circumference (–11·04 (–11·17, –10·91) cm), weighed less (–11·77 (–11·92, –11·62) kg) and had less whole-body fat mass (–7·87 (–7·97, –7·77) kg) (online Supplementary Table 3). While findings for total n-6 echoed those of LA (all P < 0·001) (online Supplementary Table 3), non-LA exhibited statistically significant associations in the opposite direction (Figure 1), though the magnitude of the relationships was smaller. Participants in the highest quintile had a slightly higher waist circumference (1·46 (1·32, 1·61) cm), were heavier (2·41 (2·25, 2·58) kg) and had more whole-body fat mass (1·81 (1·69, 1·92) kg) compared with the lowest quintile (online Supplementary Table 3). All associations remained robust and statistically significant when fatty acid levels were assessed continuously per IQ5R (online Supplementary Table 3).
The cross-sectional association between plasma linoleic acid (LA, % total fatty acids), plasma non-linoleic acid n-6 PUFA (non-LA n-6) and weight (kg), waist circumference (cm) and whole-body fat mass (kg) in the UK Biobank. Models are adjusted for age, sex, ethnicity, standing height, education, Townsend scores, physical activity and levels of DHA.
Figure 1. Long description
The forest plot presents the cross-sectional association between plasma linoleic acid (LA, percentage of total fatty acids) and plasma non-linoleic acid n-6 PUFA (non-LA n-6) with weight in kilograms, waist circumference in centimeters, and whole-body fat mass in kilograms in the UK Biobank. The plot consists of two sets of vertical forest plots. The left set shows the association with LA, while the right set shows the association with non-LA n-6. Findings for each adiposity outcome, including weight, waist circumference, and whole-body fat mass, is divided into five quintiles (Q1 to Q5). The x-axis represents standardized beta values with 95 percent confidence intervals, ranging from negative one to positive zero for LA and from negative zero point one to positive zero point two for non-LA n-6. The y-axis lists the quintiles for weight, waist circumference, and whole-body fat mass. The models are adjusted for various factors including age, sex, ethnicity, standing height, education, Townsend scores, physical activity, and levels of DHA. The plot reveals an inverse association between LA and the measured variables, with higher quintiles showing more negative beta values. Conversely, non-LA n-6 shows a positive association, with higher quintiles displaying more positive beta values.
Changes in primary anthropometric measures over time
Similar to the results from our cross-sectional analysis, anthropometric measures decreased over time with higher levels of LA (Table 2). Waist circumference, weight and whole-body fat mass for participants in the highest quintile were lower by –0·11 (–0·13, –0·09) cm, –0·05 (–0·07, –0·03) kg and –0·04 (–0·06, –0·02) kg per year, respectively. Similarly, rates of change per year by IQ5R followed the results of analyses by quintiles, showing a statistically significant decrease in all three outcomes. Results for total n-6 followed those of LA (online Supplementary Table 4). For non-LA n-6, waist circumference, weight and whole-body fat mass were higher over time. Extreme-quintile differences are an increase of 0·09 (0·07, 0·12) cm, 0·13 (0·11, 0·14) kg and 0·12 (0·10, 0·14) kg per year, respectively, in line with findings per IQ5R (Table 2).
The longitudinal association between linoleic acid and change in weight, waist circumference and whole-body fat mass in the UK Biobank
Table 2. Long description
The table presents data on the longitudinal association between linoleic acid and changes in weight, waist circumference, and whole-body fat mass in the UK Biobank. It is divided into quintiles of fatty acid, with rows for linoleic acid, non-LA n-6, and various anthropometric measures. Each quintile includes data on total participants, median percentage of total fatty acid, waist circumference, weight, and whole-body fat mass. The table shows that higher levels of linoleic acid are associated with decreases in waist circumference, weight, and whole-body fat mass over time. For participants in the highest quintile, waist circumference decreases by 0.11 centimeters per year, weight decreases by 0.05 kilograms per year, and whole-body fat mass decreases by 0.04 kilograms per year. Similar trends are observed for non-LA n-6, where waist circumference, weight, and whole-body fat mass increase over time. The extreme-quintile differences show an increase of 0.09 centimeters, 0.13 kilograms, and 0.12 kilograms per year, respectively. The table also includes P-values for trend and per IQ5R, indicating statistically significant decreases for LA and increases for non-LA in all three outcomes.
LA, linoleic acid; IQ5R, interquintile range.
Models are adjusted for age, sex, ethnicity, standing height, baseline weight, baseline waist circumference, baseline whole-body fat mass, education, Townsend scores, physical activity and levels of DHA.
* P-trend is generated by assigning participants the median value in each quintile and then assessing quintiles as continuous variables.
† Interquintile range (IQ5R) is the difference between the first and fifth quintiles.
‡ Findings here are reported as non-standardised betas (95 % CI), the difference in comparison with the lowest quintile in terms of change per year. Negative values mean that the outcome of interest is smaller compared with the reference group (set as zero), while positive values indicate the opposite.
Associations between n-6 levels and secondary outcomes
Cross-sectional associations with secondary outcomes (BMI, whole-body fat-free mass, trunk fat mass and trunk fat percent) are reported in online Supplementary Table 5. In brief, the direction of associations aligned with the primary outcomes; that is, a higher level of LA and total n-6 was linked to a lower BMI, whole-body fat-free mass, trunk fat mass and trunk fat percent, while non-LA n-6 demonstrated a statistically significant relationship in the opposite direction. For longitudinal associations, minor differences were present in comparison with primary outcomes (online Supplementary Table 6). For example, the association between LA and whole-body fat-free mass was neutral, and total n-6 was linked to increased levels of whole-body fat-free mass, but not trunk mass or trunk percent fat.
Evidence of non-linearity
Restricted cubic splines showed evidence of non-linearity for the cross-sectional relationships between LA and all three primary outcomes (all three P < 0·001). At lower LA values (20–25 % of total fatty acids), associations showed a weaker inverse association with waist circumference, weight and whole-body fat mass (online Supplementary Figures 2–4), which strengthened as LA levels increased beyond 25 %, though it weakened again beyond 35 %. Similarly, all longitudinal models showed evidence of significant non-linearity (all three P < 0·05). Specifically, LA had the strongest relationships with yearly changes in waist circumference, weight and whole-body fat mass (online Supplementary Figures 5–7) at levels of LA beyond 32 %, with less evidence of a relationship for levels of LA less than 29 %.
Interactions with age and sex
Cross-sectionally, the strength of the inverse relationship between LA waist circumference, weight and whole-body fat mass was statistically significantly stronger (all five P < 0·001) among participants who were younger or female (online Supplementary Table 7), in comparison with older or male participants, respectively. In longitudinal analyses, only weight change (with age; P < 0·05) and waist circumference change (with sex; P < 0·001) had statistically significant interactions. The stronger inverse relationship between LA and waist circumference was only present in participants who were female. Similarly, the stronger inverse association between LA and body weight was only present in those who were younger (online Supplementary Table 8).
Sensitivity analysis
When whole‑body fat mass and fat‑free mass were mutually adjusted in the cross‑sectional models, associations were attenuated but remained statistically significant. Plasma LA was linked to a 3·43 kg lower whole‑body fat mass (95 % CI –3·49, –3·36) compared with a 6·93 kg lower fat mass (95 % CI –7·02, –6·85) without mutual adjustment. For fat‑free mass, the association shifted to –0·78 (–0·82, –0·74) kg v. –3·38 (–3·43, –3·33) kg without mutual adjustment. In longitudinal analyses, no appreciable differences were observed; for example, the estimated change in whole‑body fat mass was –0·03 (–0·05, –0·02) kg compared with –0·04 (–0·06, –0·03) kg without adjustment.
Discussion
In our prospective cohort of over a quarter million participants, cross-sectional analyses showed that plasma LA and total n-6 PUFA were inversely associated with waist circumference, weight and whole-body fat mass. By contrast, higher levels of non-LA n-6 were linked to greater waist circumference, weight and whole-body fat mass, though the absolute values were small. Longitudinal analyses assessing annual change largely corroborate the cross-sectional findings. Findings for secondary outcomes (BMI, whole-body fat-free mass, trunk fat mass and trunk fat percent) are also aligned with primary outcomes. We also observed evidence for non-linearity in the associations between LA and waist circumference, weight and whole-body fat mass. All associations remain inverse and statistically significant after accounting for interactions with age and sex, although the strength of associations varied across subgroups.
Our findings show that circulating levels of plasma LA are inversely associated with waist circumference, weight and whole-body fat mass, supporting its potential protective role in body-weight regulation. Randomised controlled trials reinforce this biologic plausibility, demonstrating that LA may favourably influence adiposity through mechanisms such as improved insulin resistance(Reference Summers, Fielding and Bradshaw16,Reference Belury, Cole and Bailey23) , maintenance of lean tissue(Reference Summers, Fielding and Bradshaw16,Reference Belury, Cole and Bailey23) , reducing visceral fat(Reference Rosqvist, Iggman and Kullberg17,Reference Bjermo, Iggman and Kullberg41) and attenuating chronic inflammation(Reference Belury, Cole and Bailey23,Reference Su, Liu and Chang42,Reference Johnson and Fritsche43) . Dietary fortification of LA directly increases the level of LA in the blood and tissue(Reference Cole, Angelotti and Sparagna44), and diet is the primary source of LA, despite minor influences from genetics(Reference Tintle, Pottala and Lacey45) or biological interplay with n-3 PUFA(Reference Shearer and Walker46). Circulating LA is strongly associated with dietary LA, with the strongest dose–response association peaking at ≈8 % of total daily energy from dietary LA, though the association plateaus beyond 8 %(Reference Wu, Lemaitre and King47). Mechanistically, LA and its oxylipin metabolites act on G protein–coupled receptors and PPAR (PPARα, PPARβ/δ, PPARγ), modulating downstream pathways governing energy production and utilisation(Reference Belury9). In addition, through the cytochrome P450 pathway, LA-derived vicinal diols (9,10-dihydroxy-9Z-octadecenoic acid, 12,13-dihydroxy-9Z-octadecenoic acid) are inversely associated with adiposity(Reference Pickens, Sordillo and Zhang48,Reference Lynes, Leiria and Lundh49) , while levels of LA-derived epoxides (9(10)-epoxyoctadecenoic acid, 12(13)-epoxyoctadecenoic acid) are lower in subjects with metabolic syndrome(Reference Shearer, Borkowski and Puumala50).
In contrast, plasma non-LA n-6 PUFA were positively associated with waist circumference, weight and whole-body fat mass, albeit with small effect sizes. Interpretation is challenging, as the non-LA n-6 fraction consists of several different n-6 PUFA, including the predominant AA, as well as dihomo-gamma-linolenic acid, γ-linolenic acid, adrenic acid and Osbond acid. While AA is known to exhibit pro-inflammatory effects, some of its metabolites (PG E2) are also inflammation resolvers (inhibition of TNF-α; inducing production of lipoxin A4)(Reference Innes and Calder7). Similarly, oxylipins derived from other non-LA n-6, for example, dihomo-gamma-linolenic acid, may exert effects distinct from those of AA(Reference Gabbs, Leng and Devassy51). Hence, the findings on non-LA n-6 provide limited insight because the individual fatty acid associations cannot be evaluated.
Nevertheless, several takeaways can be drawn. If we consider findings for total n-6, the overall association is inverse, suggesting that the strong inverse association with plasma LA offsets the weak positive association of non-LA n-6 PUFA, resulting in an overall benefit. Conversely, it can also be said that investigating total n-6 masks the positive association between non-LA n-6 PUFA and adiposity. The divergence between LA and non-LA n-6 fractions challenges the common practice of pooling all n-6 PUFA into a single, global n-6 biomarker. Not all n-6 FA exert similar metabolic effects(Reference Shearer and Walker46). Hence, investigating individual n-6 FA, rather than treating n-6 PUFA as a homogeneous group, should be prioritised. Future investigations to elucidate how non-LA n-6 PUFA may influence adiposity, including their roles in eicosanoid production and inflammatory signalling, are warranted. Regardless, our findings potentially support current dietary guidelines(52) that emphasise LA-rich foods, such as nuts, seeds, soyabeans, corn and sunflower oils, as part of a balanced diet for weight maintenance.
The non-linear associations between plasma LA and adiposity are interesting: cross-sectional analyses suggest threshold effects, with the strongest inverse associations observed up to ∼35 % of total fatty acids, beyond which additional LA confers little incremental benefit. This pattern is consistent with the behaviours of other FA(Reference Mozaffarian53), where endogenous regulation limits saturation and excess accumulation(Reference Innes and Calder7). In contrast, longitudinal analysis reveals a non-linear curve, suggesting that higher LA levels beyond this threshold may still confer benefits for changes in weight and whole-body fat mass, though replication in other studies is needed to confirm this finding. Fat mass and fat-free mass are physiologically interdependent, hence attenuation after mutual adjustment suggests that cross-sectional associations with each compartment may partly reflect shared variance rather than distinct biological pathways. This interdependence underscores the need for caution when interpreting fat mass-specific cross-sectional effects. The longitudinal findings (largely unchanged with or without mutual adjustment) likely provide a more reliable indication of the relationship between LA and change in adiposity. Interactions by age and sex indicate heterogeneity in effect sizes. In particular, stronger inverse associations in females could be linked to an upregulation in PUFA metabolism through female-related sex hormones(Reference Harris, Tintle and Manson54), underscoring the need to consider demographic stratification in future investigations.
Several studies have examined the association between n-6 PUFA and body anthropometrics, yet findings remain inconsistent. In analyses based on dietary intake, total n-6 PUFA, LA and AA were generally not associated with indices of adiposity, weight gain, body fat and relative fat mass(Reference Chen, Ao and Lan28,Reference Karami, Hadi and Mohit31–Reference Jakobsen, Madsen and Dethlefsen34) . An exception is a cross-sectional US national survey, which reported an inverse association between total n-6 intake and body fat percentage(Reference Yang, Lan and Yang55). Studies using circulating biomarkers provide a somewhat clearer picture. One study that examined levels of cholesterol ester LA found significant inverse associations with sagittal abdominal diameter, waist circumference and waist:hip ratio(Reference Alsharari, Riserus and Leander25), supporting our main findings. By contrast, higher levels of circulating LA were positively linked to lean tissue volume(Reference Rosqvist, Cedernaes and Martinez Mora18,Reference Belury, Cole and Bailey23) and skeletal muscle mass(Reference Su, Zhang and Hu24), which diverges from our secondary findings on fat-free mass. Though, notably, we estimated fat-free mass via bioimpedance rather than dual-energy X-ray absorptiometry. Only one study evaluated both cross-sectional and longitudinal associations(Reference Hastert, de Oliveira Otto and Lê-Scherban26). LA, but not total n-6 PUFA or AA, was inversely associated with BMI cross-sectionally, whereas longitudinally (with a linear assumption), total n-6 PUFA were positively associated with BMI change, with null findings for LA and AA(Reference Hastert, de Oliveira Otto and Lê-Scherban26). Such inconsistencies across studies likely reflect variation in exposure assessment (dietary v. circulating biomarkers), outcome measurement (dual-energy X-ray absorptiometry v. bioimpedance) and population characteristics. Against this backdrop, our study – the largest to date to our knowledge – extends and supports existing evidence by examining both cross-sectional and longitudinal associations of n-6 PUFA, particularly plasma LA, with seven anthropometric outcomes.
Several strengths warrant emphasis. First, our investigation benefited from a substantial sample size (over a quarter million cross-sectionally), which provided robust statistical power to detect associations. Second, the use of objective fatty acid biomarkers rather than self-reported intake minimised reporting bias, strengthening the validity of exposure assessment. Third, comprehensively characterised exposures, outcomes and potential confounding variables measured via standardised protocols enhanced comparability and reproducibility. Lastly, the availability of repeated anthropometric measurements allowed us to assess associations in both cross-sectional and longitudinal frameworks, providing insights into both baseline relationships and prospective changes over time.
Nonetheless, some limitations should be acknowledged. Non-LA n-6 FA were not individually quantified, precluding a more detailed investigation of their specific roles. We also did not explore the relationship between dietary intake of PUFA and circulating biomarkers. Bioimpedance is less precise compared with dual-energy X-ray absorptiometry, but these methods have been shown to be strongly correlated at a population level(Reference Feng, Besevic and Conroy56). Participants were predominantly middle-aged to older adults, possibly limiting generalisability to younger populations. The UKBB also predominantly consists of White individuals and those with a higher socio-economic status; while these factors were accounted for in the analyses, some degree of selection bias may remain. As with all observational research, causality cannot be inferred, and residual confounding cannot be entirely excluded. However, our findings may provide insights to inform future randomised controlled trials and intervention studies.
In conclusion, higher levels of total n-6 PUFA, particularly plasma LA, were associated with smaller waist circumference and lower weight and whole-body fat mass in both cross-sectional and longitudinal analyses. In contrast, non-LA n-6 demonstrated associations in the opposite direction with weak effect sizes. Our findings potentially support dietary recommendations to include LA-rich oils in the diet. Furthermore, the divergent patterns between LA and non-LA n-6 highlight the limitations of relying on a global total n-6 biomarker, underscoring the need for future studies to investigate the specific determinants and potential health implications of individual non-LA n-6 fatty acids.
Supplementary material
For supplementary material/s referred to in this article, please visit https://doi.org/10.1017/S0007114526107430.
Acknowledgements
None.
This research was supported in part by R01 HL089590 (WSH, PI) and by a grant (25–108-D-A-1-A) from the Soy Nutrition Institute Global with support from the United Soybean Board. The Soy Nutrition Institute Global and the United Soybean Board had no role in the design, analysis or writing of this article.
The authors’ responsibilities were as follows – W. S. H. and N. L. T. conceived the project; H. T. M. L., J. W., N. L. T., M. A. B. and W. S. H. refined the study protocol; N. L. T. gained access to the UK Biobank dataset; J. W., E. D. J. and N. L. T. analysed the data; all authors interpreted the findings; H. T. M. L. prepared the first draft, which J. W., N. L. T., M. A. B. and W. S. H. improved by critical review; H. T. M. L. and W. S. H. had primary responsibility for final content; and all authors read and approved the final manuscript.
W. S. H. is the founder and President of OmegaQuant Analytics, LLC, which offers blood fatty acid testing to researchers, healthcare providers and consumers. M. A. B. serves on the Board of Trustees for the American Society for Nutrition Foundation. H. T. M. L., J. W., E. D. J. and N. L. T. have no conflicts of interest to disclose.
UKBB has ethical approval (Ref. 11/NW/0382) from the North West Multicentre Research Ethics Committee. Use of these de-identified, publicly available data for research was approved by the University of South Dakota Institutional Review Board (IRB-21-147). All participants provided electronically signed informed consent. The UKBB study was conducted according to the guidelines laid down in the Declaration of Helsinki. The UKBB protocol is available online (https://www.ukbiobank.ac.uk/wp-ontent/uploads/2011/11/UK-Biobank-Protocol.pdf).
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