Multimorbidity, the existence of two or more concurrent chronic conditions in a single individual, represents a major global health challenge. The Nutrition Society’s 2023 Winter Conference at the Royal Society, London focused on the topic of ‘Diet and lifestyle strategies for prevention and management of multimorbidity’, with symposia designed to explore pathways for prevention of multimorbidity across the lifecourse, the role of ageing, the gut-brain-heart connection and lifestyle strategies for prevention and management of multimorbidity. It also considered machine learning and precision nutrition approaches for addressing research challenges in multimorbidity. The opening plenary lecture discussed advancing diet and lifestyle research to address the increasing burden and complexity of multimorbidity. The two-day programme concluded with a plenary which addressed the key dietary risk factors and policies in multimorbidity prevention.

Associations between obesity and mental illness have been identified, but they are complex and bidirectional(1). Weight loss interventions have been proposed as a potential strategy to improve mental health in individuals with overweight or obesity, but the evidence remains inconclusive(2). Additionally, the role of specific foods in a weight loss diet and mental health outcomes is not well understood(3). This study aimed to explore the association between weight loss (with and without almonds) and self-administered psychological and sleep assessments, including the Profile of Mood States (POMS), the Perceived Stress Scale (PSS), the Zung Self-Rating Depression Scale (ZSDS), and the Pittsburgh Sleep Quality Index (PSQI). Participants (n = 140, 47.5 ± 10.8 years) with overweight or obesity (BMI: 30.7 ± 2.3 kg/m2) were randomised to an energy-controlled almond-enriched diet (AED) or nut-free diet (NFD). Psychological and sleep assessments were conducted at baseline, after 3 months of weight loss, and after 6 months of weight maintenance. Data were analysed using mixed-effects models and linear regression. For POMS, total mood disturbance score (TMDS) (60.2%, p = 0.01), fatigue-inertia (21.2%, p = 0.003), and vigor-activity (19.9%, p<0.001) improved over time (with no different between groups), with improvements associated with the magnitude of weight loss (TMDS: β = 0.059, p = 0.02; fatigue-inertia: β = 0.268, p = 0.016; vigor-activity: β=-0.194, p = 0.048). No significant changes were observed in tension-anxiety, depression-dejection, anger-hostility, or confusion-bewilderment. A significant group x time interaction (p = 0.048) was found for the PSS, which increased in the NFD group (10.1%) and decreased in the AED (1%) during the weight maintenance phase. No significant changes were observed for the ZSDS. The PSQI demonstrated significant improvement in both groups over time for sleep quality (11.3%, p<0.001), sleep latency (24.3%, p<0.001), sleep disturbance (39.2%, p = 0.04), and daytime dysfunction (290.4%, p<0.001), but not for sleep duration or habitual sleep efficiency. Summed scores, generating the global sleep score (GSS), demonstrated an overall significant improvement in both groups over time (33.5%, p<0.001), and these improvements were associated with weight loss (GSS: β = 0.863, p<0.001). The findings emphasise the importance of evaluating mental health outcomes in weight loss interventions and highlight the potential influence of weight management on mood and sleep quality. Further research is warranted to explore the impact of diet composition on perceived stress and other mental health outcomes.

Obesity and COVID-19 are global pandemics listed by World Health Organization, which need urgent attention. Obesity involves low grade chronic inflammation, which is characterised by sustained pro-inflammatory innate immune responses mediated through activation of the NLR family pyrin domain-containing 3 (NLRP3) inflammasome/IL-1 axis, and is a strong risk factor for Diabetes and Heart diseases(1). Dietary fats provide energy, satiety, source of fat-soluble vitamins and essential fatty acids – Omega 3 (n-3) and Omega 6 (n-6) fatty acids, but must be consumed in right amounts and ratios. Essential fatty acids (EFA) deficiency and n-6/n-3 imbalance is linked with chronic illnesses such as, heart attacks, cancer, insulin resistance, stroke, obesity, and diabetes(2). Excess dietary fat intake and imbalance of fatty acids, contribute to obesity, inflammation, comorbidities and faster disease progression. A cross-sectional survey aimed to understand the fatty food frequency of overweight and obese middle age adults from Mumbai, India during the COVID-19 outbreak. Using purposive sampling, 100 adults (30-60 years), a questionnaire (demographics, anthropometrics and fat food frequency questionnaire) was administered. Data was analysed using SPSS 26.0. As per BMI standards, 60.2% participants were overweight, 12.6% were obese and 27.1% had normal BMI. For visible fat consumption, sunflower oil (47.6%), ghee (38.8%), rice bran oil (34%) groundnut oil (11.7%) and invisible fats – milk (100%). Other dairy products, nuts and oilseeds were consumed weekly. twice a week, majority (92.3%) consumed packaged high fat foods as compared to eating deep-fried items (58.9%). We concluded that excess dietary fat intake is high risk factor for obesity and related comorbidities diabetes, and hypertension. High BMI increases the risk for non-communicable diseases (NCDs) such as obesity, cardiovascular disease (CVD), insulin resistance and type 2 diabetes. People with co-morbidities are high risk groups for COVID-19 infection susceptibility. Hence, managing weight could be a cost-effective preventive strategy to help in delaying the onset and progression of NCDs, thereby lowering the susceptibility to COVID-19. Our findings have important implications in working towards adopting healthy fats and reducing mortality and reducing the global burden of pandemic. High dietary fat intake is a modifiable risk factor for overweight and obesity. Comorbidities increased risk for COVID-19 infection, disease severity and mortality. Hence, there is a need to understand the dietary fat consumption patterns in obesity and COVID-19. Dietary carbohydrate, sugar and fat quality in relation to obesity and pandemic such as, COVID-19 could be explored in future studies.

The aim of this study was to explore associations between habitual dietary fibre intake, adiposity, and biomarkers of metabolic health in Pacific and New Zealand European women who are known to have different metabolic disease risks. Pacific (n = 126) and New Zealand European (NZ European; n = 161) women (18-45years) were recruited to the PROMISE cross-sectional study(1) based on normal (18-24.9kg/m2) and obese BMI (≥30kg/m2). Body fat percentage (BF%), measured using whole body DXA, was used to stratify participants into low (<35%) or high (≥35%) BF% groups. Habitual dietary intake was calculated using the National Cancer Institute method, involving a 5-day-food-record and a semi-quantitative FFQ. Fasting blood was analysed for glucose, insulin, and lipid profile. NZ European women in the low- and high-BF% groups were older, less socioeconomically deprived, and consumed more dietary fibre (median 23.7g/day [25-75-percentile, 20.1, 29.9]; 20.9 [19.4, 24.9]) than Pacific women (18.8 [15.6, 22.1]; 17.8 [15.0, 20.8]; both p<0.001), respectively. Pacific women consumed a higher proportion of their total fibre intake from discretionary fast foods, in contrast NZ European women consumed more dietary fibre from wholegrains. Regression analysis controlling for ethnicity, age, socioeconomic deprivation, energy intake, protein, total carbohydrate, and fat intake showed significant inverse associations between higher dietary fibre intake and BF% and visceral fat% (β = −0.47, 95% CI = −0.62, −0.31, p<0.001; β = −0.61, [−0.82, −0.40], p<0.001, respectively) among both Pacific and NZ European women. LDL-C (β = −0.04, [−0.06, −0.01]) was inversely associated with fibre intake following further adjustment for BF%-groups in NZ European women. Despite differences in intake, dietary fibre was inversely associated with adiposity and metabolic disease risk in both Pacific and NZ European women. However younger woman living in areas of higher socio-economic deprivation who consumed a higher proportion of total dietary fibre intake from discretionary fast foods were more likely to have low dietary fibre intakes than older, wealthier women. These women were also more likely to be Pacific women. Increasing habitual dietary fibre intake could help to reduce adiposity and metabolic disease risk; so implementing policies that make health-promoting high fibre foods more affordable, ensuring households have sufficient income to purchase nutritious food and limiting the amount of unhealthy food marketing that low income communities are exposed to should be public health priorities.

Nutrition transitions are key contributors to the obesity epidemic plaguing South Pacific Island countries (SPIC). Prior to European contact and colonisation, traditional Pacifica diets consisted mainly of root crops, indigenous fruits and vegetables, freshwater proteins and seafood(1). This diet has been replaced by diets high in processed foods which are high in salt, sugar and unhealthy fats(2). Various political, economic, environmental and socio-cultural factors have been associated with the proliferation of unhealthy foods in Pacifica diets. However, very few studies have examined how these changes have impacted individual food choices. This study aims to address the gap in the knowledge of food choice motives of Pacific Islanders. An online qualitative survey was used(1) to explore how the nine food choice motives in the widely used Food Choice Questionnaire (FCQ)(3) impact food choice and (2) to identify additional food choice motives, not captured in the FCQ. The first section of the survey included open-ended questions which explored the top three food choice motives of the participants. This section was followed by a series of open-ended questions exploring participant’s views on the nine food choice motives from the FCQ. The last part of the survey asked participants to identify any other food choice motives which were not already identified in the survey. An exploratory qualitative approach, employing inductive and deductive thematic analyses, was used to analyse results(4). The sample (N = 105) was predominantly female (73%), living with family (72%) and the average age was 22.06 years (S.D = 5.0). It consisted of 28% indigenous Fijians, 26% Fijians of Indian descent, 15% Solomon Islanders, 13% I-Kiribati and smaller percentages of students from Niue (3%), Samoa (5%), Tonga (5%) and Vanuatu (5%). Of the nine FCQ motives, the most commonly identified top three food choice motives included price (n = 54), health (n = 45), and sensory appeal, especially taste (n = 40). Participants also identified three new food choice motives which many ranked in their top three motives: satiety concerns (n = 22), food quality and hygiene when eating out (n = 26) and religious or cultural food restrictions (n = 11). Additionally, issues with face validity of the health, convenience, price, weight control and familiarity food choice motives from the FCQ were identified. These findings highlight the importance of validity studies prior to using FCQ, and more broadly other similar instruments, with understudied populations like that of SPIC. The findings also provided important insights into the food choice motives of Pacific Islanders and can inform public health interventions for encouraging healthy eating. Further research using an adapted FCQ with larger and diverse samples will increase its effectiveness of measuring food choice motives in the region.

Depression and eating disorder (ED) risk are heightened during adolescence(1) and both were exacerbated during COVID-19 lockdowns. This analysis reports changes in self-reported symptoms of depression and eating disorders throughout the Fast Track to Health trial. Fast Track to Health was a 52-week multi-site randomised-controlled trial, conducted 2018-2023, comparing intermittent (IER) and continuous energy restriction (CER) in adolescents with obesity and ≥1 associated comorbidity(2). The Centre for Epidemiologic Studies Depression Scale-revised 10-item version for adolescents (CESDR) was used to assess symptoms of depression (no symptoms, sub-threshold, or possible, probable, major depressive episode). Eating Disorder Examination Questionnaire (EDE-Q) was used to assess ED risk; defined as global score ≥2.7, ≥2 episodes of binge eating with/without loss of control, or ≥1 episode of purging within the last 28-days. The Binge Eating Scale (BES) assessed severity of binge eating (no binge eating, mild/moderate, severe). Adolescents were monitored for disordered eating during dietetic consults. Linear mixed models, retaining all data consistent with intention-to-treat analysis, were used to estimate the change in outcomes from baseline to week-52. Descriptive statistics were used to describe the number of participants meeting screening criteria at baseline and week-52. One hundred and forty one adolescents were enrolled and 97 completed the trial, with median (IQR) EDE-Q score 2.28 (1.43 to 3.14), CESDR 9.00 (4.0 to 14.5) and BES 11.0 (5.0 to 17.0) at baseline. EDE-Q (change in estimated marginal means [SE], IER −0.63 [0.18], CER −0.56 [0.17]) and CESDR (IER −2.70 [1.15], CER −3.87 [1.07]) scores reduced between baseline and week-52 in both groups (p<0.05) with no difference between groups. There was a between group difference (p = 0.019) in change in BES. The IER group had a reduction between baseline and week-52 (−3.72 [1.27]) and the CER group had no change. At baseline, 31 (22%) adolescents were classified as having a possible/probable/major depressive episode, 110 (78%) met ≥1 ED criteria and 28 (21%) as mild/moderate or severe binge eating, reducing to 8 (9%), 56 (61%) and 15 (16%) respectively at week-52. A small sub-group of adolescents required additional support for disordered eating. Overall, treatment-seeking adolescents with obesity have symptoms of depression and ED. Although symptoms reduce for most, some required additional support. Screening and monitoring for depression and ED are important to ensure early intervention.

The global pandemic of paediatric overweight and obesity, along with undernutrition among children in low-income countries pose challenges for future health. Unhealthy dietary intake among children is of great concern(1). The aim of this study was to determine the association between intakes from healthy and unhealthy food groups and adiposity among 5- to 9-year-old South African children (n = 920). Conventional dietary intake assessment methods are burdensome; therefore, a short unquantified food frequency questionnaire was developed based on the WHO Global school-based student health survey, which focused on healthy and unhealthy food groups. The new questionnaire includes four healthy food groups (fruits, vegetables, milk, meats) and six unhealthy food groups (hot sugar-sweetened beverages (SSBs), cold SSBs, cookies, candies, salty snacks, fast foods) with five different responses of frequency of intake per week. The food groups reflect foods generally eaten by South African school children. The questionnaire was completed by the parents. Weight and height were measured and WHO BMI z-score (BAZ) was calculated(2). Descriptive statistics were reported using median and interquartile range. Frequency of intakes from food groups were compared across tertiles of BAZ using the Kruskal-Wallis test. The correlation between frequency of intakes from different food groups, and between the food groups and BAZ was calculated. The children reported similar daily intakes from the milk (35.3%), cold SSBs (33%) and hot SSBs groups (27%). Fruit (14%) and vegetables (9.6%) were consumed daily by a small percentage of children, while animal source protein foods (meat, fish, poultry, eggs) were consumed daily by 39% of children. The most frequent daily consumed snacks were salty snacks, e.g. crisps (13.2%), candy (11.1%) and cookies (5.3%), while fast foods were consumed once per week by the largest proportion of children (60.7%). Based on the WHO BMI z-scores, 15.2% of children were overweight, 4.4% were obese and 3.8% were underweight. Children in the highest two tertile groups of BAZ had a higher median weekly frequency of SSB intake (5, IQR 1,7), compared to those in the lowest BAZ tertile (3, IQR 1,7). No other differences were found between frequency of food group intake across BAZ tertiles. There was a weak positive correlation between BAZ and the frequency of SSB intake (r = 0.08, = 0.015), as well as between frequency of milk intake and frequency of SSB intake (r = 0.13, P<0.001), but a weak negative correlation between the frequency of vegetable intake and frequency of SSB intake (r = −0.08, P = 0.01). In conclusion, low fruit and vegetable intakes, combined with regular SSB intakes are evident in this group of children. The frequency of SSB intake was positively associated with adiposity, and SSB intake apparently replaced vegetable intake, but not milk intake among the children.

Energy restricted diets improve liver function(1) and habitual nut consumption has been associated with a lower prevalence of fatty liver(2). This study examined the effect of incorporating almonds in an energy restricted diet on liver health biomarkers. One Hundred and forty adults (42M:98F, 47.5 ± 10.8 years, BMI 30.7 ± 2.3 kg/m2) enrolled in a 9-month (9M) dietary intervention comprising 3 months (3M) weight loss (30% energy restriction) followed by 6 months (6M) of weight maintenance. Participants were randomly assigned to consume almonds (n = 68, AED) or isocaloric carbohydrate-rich snacks (n = 72, CRD) which provided 15% of total daily energy. At baseline (BL), 3M and 9M, fatty liver index (FLI) scores (0-100)(3) were calculated using body mass index (BMI), waist circumference (WC), fasting serum gamma-glutamyl transferase (GGT) and triglyceride (TAG) levels, and other liver health biomarkers were assessed by ultrasound (volume, visual appearance and elastography (a marker of stiffness due to fibrosis)). Intention to treat analyses were conducted using mixed effects modelling (fixed effects group and time, with participants as the random effect). Significant reductions from BL occurred over time (all p<0.001 for 3M and 9M) with no difference between groups (AED vs CRD, P>0.05) in BMI (3M: −2.44 ± 0.20 vs −2.32 ± 0.20, 9M: −2.83 ± 0.19 vs −2.81 ± 0.19 kg/m2), WC (3M: −8.04 ± 0.79 vs −7.00 ± 0.81, 9M: −8.72 ± 0.83 vs −9.14 ± 0.81 cm), TAG (3M: −0.24± 0.08 vs −0.22 ± 0.09, 9M: −0.37 ± 0.09 vs −0.21 ± 0.09 mmol/L), FLI score (3M: −23.8 ± 2.0 vs −17.6 ± 2.1, 9M: −23.8 ± 2.0 vs −17.6 ± 2.1), and liver volume (3M: −134.56 ± 38.30 vs −100.96 ± 37.25, 9M: −113.68 ± 37.42 vs −110.64 ± 35.47cm3). Significantly greater reductions occurred for AED compared to CRD at 3M and 9M in GGT (p = 0.003) (3M: −9.68 ± 1.93 vs −0.01 ± 2.00, 9M: −7.75 ± 2.06 vs −2.78 ± 2.15 IU/L) and liver visual assessment scores (p = 0.03) (3M: −0.58 ± 0.24 vs −0.45 ± 0.23, 9M: −1.33 ± 0.23 vs −0.50 ± 0.22). There were no significant changes in liver elastography over time or between groups. Energy restriction improved body composition and reduced the extent of fatty liver and liver size but did not change liver stiffness. The inclusion of almonds in an energy restricted diet demonstrated additional benefits to some liver health biomarkers providing support for almonds being incorporated into lifestyle interventions to improve liver function.

The role of protein in decreasing the risk of cardiometabolic diseases has been proposed, yet the findings are inconsistent, possibly due to how protein intake was expressed(1). This study aimed to examine how different ways of expressing protein intake may influence its relationships with diet quality and obesity. This study used data from the Australian National Nutrition and Physical Activity Survey (NNPAS) 2011-12, focusing on adults aged ≥19 years, excluding those who were pregnant or lactating, and had both anthropometric and dietary data (n = 7637). Total protein intake was assessed by up to two 24-hour dietary recalls and reported in two measurement units, namely g/day and % of energy intake. Usual protein (g) and energy intakes (kJ) were modelled using the Multiple Source Method(2). Diet quality was assessed using the Dietary Guidelines Index (DGI)(3). Body mass index (BMI) and waist circumference were used as measures of obesity. Multiple linear regressions were performed stratified by sex, and adjusting for age, country of birth, Socio-economic Indexes for Areas, physical activity level, energy misreporting, usual energy intake (for diet quality), and non-protein energy intake (for obesity). Positive associations between protein intake and diet quality were confirmed across measurement units. Protein intake expressed in % of energy intake (β-coefficient: men = 0.97; women = 1.21, p<0.001) had a higher β-coefficient than those reported in g/day (β-coefficient: men = 0.19; women = 0.30, p<0.001). Linear regression models showed a positive association between protein intake and BMI for men and women, either expressed in g/day (β-coefficient: men = 0.02, p<0.001; women = 0.03, p = 0.001) or % of energy intake (β-coefficient: men = 0.14, p<0.001; women = 0.12, p = 0.002). The relationship between protein intake expressed in g/day and waist circumference was also statistically significant (β-coefficient: men = 0.04, p = 0.004; women = 0.05, p = 0.035). However, protein intake expressed as % of energy intake was correlated with waist circumference for men only (β-coefficient: men = 0.26, p = 0.001; women = 0.19, p = 0.075). The consistent findings across measurement units suggested that the unit of g/day and % of energy intake can be used for examining associations between protein intake and diet quality. However, associations between protein intake and measures of obesity varied between protein units and sex. These findings suggest that the selection of protein measurement units in relation to obesity outcomes might need to consider the characteristics of study population (e.g., sex, energy intake). Overall, the outcomes of this study suggest that how protein is expressed may impact the associations between protein intake, diet quality, and obesity, and therefore require further considerations when examining the role of protein in cardiometabolic health.

Prenatal vitamin D deficiency is widely reported and may affect perinatal outcomes. In this secondary analysis of the UK Pregnancies Better Eating and Activity Trial, we examined vitamin D status and its relationship with selected pregnancy outcomes in women with obesity (BMI ≥ 30 kg/m2) from multi-ethnic inner-city settings in the UK. Determinants of vitamin D status at a mean of 17 ± 1 weeks’ gestation were assessed using multivariable linear regression and reported as percent differences in serum 25-hydroxyvitamin D (25(OH)D). Associations between 25(OH)D and clinical outcomes were examined using logistic regression. Among 1089 participants, 67 % had 25(OH)D < 50 nmol/l and 26 % had concentrations < 25 nmol/l. In fully adjusted models accounting for socio-demographic and anthropometric characteristics, 25(OH)D was lower among women of Black (% difference = −33; 95 % CI: −39, −27), Asian (% difference = −43; 95 % CI: −51, −35) and other non-White (% difference = −26; 95 % CI: −35, −14) ethnicity compared with women of White ethnicity (n 1086; P < 0·001 for all). In unadjusted analysis, risk of gestational diabetes was greater in women with 25(OH)D < 25 nmol/l compared with ≥ 50 nmol/l (OR = 1·58; 95 % CI: 1·09, 2·31), but the magnitude of effect estimates was attenuated in the multivariable model (OR = 1·33; 95 % CI: 0·88, 2·00). There were no associations between 25(OH)D and risk of preeclampsia, preterm birth or small for gestational age or large-for-gestational-age delivery. These findings demonstrate low 25(OH)D among pregnant women with obesity and highlight ethnic disparities in vitamin D status in the UK. However, evidence for a greater risk of adverse perinatal outcomes among women with vitamin D deficiency was limited.

Racism shapes the distribution of the social determinants of health (SDoH) along racial lines. Racism determines the environments in which people live, the quality of housing, and access to healthcare. Extensive research shows racism in its various forms negatively impacts health status, yet few studies and interventions seriously interrogate the role of racism in impacting health. The C2DREAM framework illuminates how exposure to racism, in multiple forms, connects to cardiovascular disease, hypertension, and obesity. The goal of the C2DREAM framework is to guide researchers to critically think about and measure the role of racism across its many levels of influence to better elucidate the ways it contributes to persistent health inequities. The conceptual framework highlights the interconnectedness between forms of racism, SDoH, and the lifecourse to provide a greater context to individual health outcomes. Utilizing this framework and critically contending with the effects of racism in its multiple and cumulative forms will lead to better research and interventions.

An increase in the prevalence of obesity due to lockdown and confinement linked to COVID-19 is observed. Variations in the nutritional status of schoolchildren from Jujuy are analyzed in relation to confinement due to COVID-19 (2019–2021) and its relationship with socio-demographic variables and the school environment. This is an observational, descriptive study. Data from 56,695 schoolchildren aged 6–18 years old is analyzed based on two temporary cuts (2019 pre-confinement and 2021 post-confinement). The nutritional status of schoolchildren (underweight, overweight, and obese) was established using the IOTF (International Obesity Task Force) criterion. The prevalence of each nutritional phenotype was estimated by sex and age group, considering the following independent variables: setting (rural/urban), school management system (public/private), geographic altitude, and percentage of households with unmet basic needs (UBN) in the place where they attend school. Multiple proportions contrast was performed using Fisher's test, a transition matrix ws produced and a statistical model of proportional odds was fitted. It was observed that between 2019 and 2021, the prevalence of underweight decreased and the prevalence of overweight and obesity increased significantly. In 2021, 67% of schoolchildren maintained the same nutritional category that they had in 2019, 21% gained weight and 12% lost weight. The model explains about 52% of the total variability observed. The factors that are significantly correlated in the model are school cycle, age, geographic altitude, school setting, and % of households with UBN. The results indicate that during the COVID-19 pandemic, there was a shift to the right in the distribution of the nutritional status categories of the schoolchildren population in Jujuy, with a decrease in the prevalence of underweight and an increase in the prevalence of overweight and obesity with variations related to age, school location, geographic altitude, and socioeconomic characteristics of the households in the place where the children attended school.

(Protein–energy) malnutrition in individuals living with obesity presents complex diagnostic challenges due to the distinctive physiological characteristics of obesity. This narrative review critically examines the identification of malnutrition within the population with obesity, distinguishing malnutrition in obesity from related conditions such as sarcopenic obesity. While noting some shared features, the review highlights key differences between these conditions. The review also highlights the limitations of current malnutrition screening tools, which are not designed for individuals living with obesity. These tools primarily rely on anthropometric measurements, neglecting (among others) nutrient intake assessment, which hinders accurate malnutrition detection. Additionally, this review discusses limitations in existing diagnostic criteria, including the Global Leadership Initiative on Malnutrition (GLIM) criteria, when applied to individuals living with obesity. Challenges include the identification of appropriate cut-off values for phenotypic criteria (unintentional weight loss, low body mass index and muscle mass) and aetiological criteria such as reduced food intake and inflammation for the population with obesity. Overall, this review emphasises the need for modified screening tools and diagnostic criteria to recognise and assess malnutrition in obesity, leading to improved clinical outcomes and overall wellbeing.