Malnutrition is a key factor in metabolic syndrome (MS) and sarcopenia, assessing the nutritional status of these patients is a pressing issue. The purpose of this study was to clarify sarcopenia and sarcopenic obesity in patients with MS based on nutritional status. This was a case–control study between MS/non-MS. Body composition was measured by dual-energy X-ray absorptiometry. Muscle function was assessed by handgrip strength, five times sit-to-stand test, gait speed test and short physical performance battery (SPPB). The Mini Nutritional Assessment (MNA) was performed to assess the nutritional status in the participants in this study. Overall, a total of 56 % and 13 % of participants suffered from possible sarcopenia and sarcopenia, respectively. There was a higher rate of possible sarcopenic obesity in the MS group than in the non-MS group (48·9 % v. 24·7 %, P < 0·01), and all the sarcopenia participants in the MS group had sarcopenic obesity. MNA score was significantly associated with sarcopenia status (P < 0·01). The MNA combined with body fat score showed better acceptable discrimination for detecting sarcopenic obesity and sarcopenia in MS (AUC = 0·70, 95 % CI 0·53, 0·86). In summary, there was a higher prevalence of possible sarcopenic obesity in MS, and all the MS patients with sarcopenia had sarcopenic obesity in the present study. We suggest that the MNA should be combined with body fat percentage to assess the nutritional status of MS participants, and it also serves as a good indicator for sarcopenia and sarcopenic obesity in MS.

Malnutrition is a major problem among older adults with type 2 diabetes mellitus (T2DM). Some studies suggest that well glycaemic control increases the risk of frailty due to reduced intake. Therefore, it could be hypothesised that adequate glycaemic controlled patients may be at risk of malnutrition. This study aimed to examine, in older adults with T2DM, the association between adequate glycaemic control and malnutrition as well as identify the risk factors for malnutrition. Data including general characteristics, health status, depression, functional abilities, cognition and nutrition status were analysed. Poor nutritional status is defined as participants assessed with the Mini Nutritional Assessment as being at risk of malnutrition or malnourished. Adequate glycaemic control refers to an HbA1c level that meets the target base in the American Diabetes Association 2022 guidelines with individualised criteria. There were 287 participants with a median (interquartile range) age of 64 (61–70) years, a prevalence of poor nutrition, 15 %, and adequate glycaemic control, 83·6 %. This study found no association between adequate glycaemic control and poor nutrition (P = 0·67). The factors associated with poor nutritional status were low monthly income (adjusted OR (AOR) 4·66, 95 % CI 1·28, 16·98 for income < £118 and AOR 7·80, 95 % CI 1·74, 34·89 for income £118–355), unemployment (AOR 4·23, 95 % CI 1·51, 11·85) and cognitive impairment (AOR 5·28, 95 % CI 1·56, 17·93). These findings support the notion that older adults with T2DM should be encouraged to maintain adequate glycaemic control without concern for malnutrition, especially those who have low income, unemployment or decreased cognitive functions.

Malnutrition (synonym: undernutrition) is prevalent among older adults, which may be partly related to changes in dietary intake, but evidence on the link between malnutrition and diet is scarce. The aims of this study were to estimate the association between energy/nutrients intake and malnutrition, and to characterise nutritional inadequacy in institutionalised and non-institutionalised older adults. A national survey was conducted including a Portuguese representative sample of nursing home (NH) residents (n 563) and community-dwellers (n 837) aged ≥ 65 years. Data included socio-demographic characteristics, self-reported health, loneliness feelings, nutritional status (Mini Nutritional Assessment®) and dietary intake (two non-consecutive 24-h recalls). A higher energy intake was associated with lower odds of malnutrition risk (being ‘at risk of malnutrition’ or ‘malnourished’) in both settings, but only significant among NH residents after adjusting for confounders (NH: OR = 0·66, 95 % CI 0·50, 0·86; community: OR = 0·64, 95 % CI 0·37, 1·10). The intake of carbohydrates, fat, fibre, vitamin C, Na, K and Mg was inversely associated with malnutrition risk in NH residents, and protein, fat, vitamin B6, folates, Na, K, Ca and Mg intake in community-dwellers. After additional adjustment for total energy, only Na and Mg intake of community-dwellers remained significantly associated. The prevalence of inadequate nutrient intake was generally higher for the malnutrition risk group, which was particularly evident among community-dwellers. The effect of dietary intake on nutritional status seems more dependent on total energy and carbohydrates intake in institutionalised elders, whereas among community-dwellers protein and some micronutrients appear to have a greater impact.

Low socio-economic status (SES) is often associated with various health-related problems. Therefore, the present paper aims to review the available literature to identify the prevalence of malnutrition, prevalence of poor diet quality and its associated risk factors among older adults with low SES. A literature search was performed using four databases, namely PubMed, Google Scholar, Springer and Science Direct. The search terms used were ‘diet quality’, ‘nutritional status’, ‘dietary intake’, ‘overweight’, ‘obesity’, ‘underweight’, ‘older people’ and ‘low socioeconomic status (SES)’. The overall prevalence of undernutrition among older adults with low SES worldwide was in the range of 28·9 to 48 %, while overnutrition was reported to be between 8·1 to 28·2 %. In Asia, the prevalence of undernourished older adults ranged from 3 to 64·9 %, while 2·5 to 32·8 % were overnourished. Most of the studies (60 %) included in the present review used BMI as the tool to identify malnutrition, but none of the nutritional screening tools were considered to be the ‘gold standard’. For dietary assessment, FFQ and multiple 24 h dietary recall improved the estimation of individual dietary intake. Risk factors for poor diet quality included financial hardship, functional limitation, sex, place of residence, smoking and oral health. Poor nutritional status, especially lack of good-quality diet, and thinness are prevalent among older adults with low SES. Hence, it is important to establish nutrition-related programmes and intervention studies among this group of individuals for improving their health status and quality of life.

With the focus of care shifting from the hospital to the community, supportive nutritional care to old people is to become an important issue to address in the community, since undernutrition has serious consequences, both for the quality of life and for the health care costs. Several modifiable nutritional risk factors relate to undernutrition. Unfortunately, the problem with (risk of) undernutrition is aggravated due to a lack of alertness among e.g. health care staff, leading to insufficient attention for systemic screening and nutritional care. Only a few of the existing screening tools have been validated among old people receiving support at home. Few studies have assessed the beneficial effect of nutritional support among old people in their own home, and recently, it was concluded that such have shown limited effects. One reason may be that the nutritional interventions performed have not taken the multiple nutritional risk factors afore-mentioned into consideration when formulating the action/treatment plan and hence not used a multidisciplinary approach. Another reason may be that the intervention studies have not used validated screening tools to identify those old people most likely to benefit from the nutritional support. However, three recent studies have used a multidisciplinary approach and two have proven a beneficial effect on the quality of life of the old people and the health care costs. These findings suggest that when planning nutritional intervention studies for old people receiving support at home, modifiable nutritional risk factors should be taken into consideration, and a multidisciplinary approach considered.

During and after hospitalisation, older adults are recommended to consume 1·2–1·5 g of protein/kg body weight per d (g/kg per d) to improve recovery. This randomised controlled trial studied the effectiveness of a 12-week intervention with protein-enriched foods and drinks by following-up seventy-five older patients (mean age: 76·8 (sd 6·9) years) during their first 6 months after hospital discharge. Primary outcomes were protein intake and physical performance (measured with Short Physical Performance Battery (SPPB)). Secondary outcomes for physical recovery were gait speed, chair-rise time, leg-extension strength, hand-grip strength, body weight, nutritional status (Mini Nutritional Assessment), independence in activities of daily living (ADL) and physical activity. The intervention group consumed more protein during the 12-week intervention period compared with the control group (P<0·01): 112 (sd 34) g/d (1·5 (sd 0·6) g/kg per d) v. 78 (sd 18) g/d (1·0 (sd 0·4) g/kg per d). SPPB total score, gait speed, chair-rise time, body weight and nutritional status improved at week 12 compared with baseline (time effect P<0·05), but were not different between groups. Leg-extension strength, hand-grip strength and independence in ADL did not change. In conclusion, protein-enriched products enabled older adults to increase their protein intake to levels that are higher than their required intake. In these older adults with already adequate protein intakes and limited physical activity, protein enrichment did not enhance physical recovery in the first 6 months after hospital discharge.

Malnutrition predicts preterm death, but whether this is valid irrespective of the cause of death is unknown. The aim of the present study was to determine whether malnutrition is associated with cause-specific mortality in older adults. This cohort study was conducted in Sweden and included 1767 individuals aged ≥65 years admitted to hospital in 2008–2009. On the basis of the Mini Nutritional Assessment instrument, nutritional risk was assessed as well nourished (score 24–30), at risk of malnutrition (score 17–23·5) or malnourished (score <17). Cause of death was classified according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision, into twenty different causes of death. Data were analysed using Cox proportional hazards regression models. At baseline, 55·1 % were at risk of malnutrition, and 9·4 % of the participants were malnourished. During a median follow-up of 5·1 years, 839 participants (47·5 %) died. The multiple Cox regression model identified significant associations (hazard ratio (HR)) between malnutrition and risk of malnutrition, respectively, and death due to neoplasms (HR 2·43 and 1·32); mental or behavioural disorders (HR 5·73 and 5·44); diseases of the nervous (HR 4·39 and 2·08), circulatory (HR 1·95 and 1·57) or respiratory system (HR 2·19 and 1·49); and symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (HR 2·23 and 1·43). Malnutrition and risk of malnutrition are associated with increased mortality regardless of the cause of death, which emphasises the need for nutritional screening to identify older adults who may require nutritional support in order to avoid preterm death.

A higher fat content in the diet could be an advantage for preventing malnutrition among older adults. However, there is sparse scientific evidence to determine the optimal fat intake among older adults. This prospective cohort study examined whether a high energy intake of dietary fat among middle-aged and older adults is associated with the risk of malnutrition 10 years later. The study population comprised 725 Swedish men and women aged 53–80 years who had completed a questionnaire about dietary intake and lifestyle factors in 1997 (baseline) and whose nutritional status was assessed when admitted to the hospital in 2008–2009 (follow-up). At the follow-up, 383 (52·8 %) participants were identified as being at risk of malnutrition and fifty-two (7·2 %) were identified as malnourished. Multinomial logistic regression models were used to analyse the association between previous dietary fat intake and nutritional status later in life. Contrary to what was expected, a high energy intake from total fat, saturated fat and monounsaturated fat among middle-aged and older adults increased the risk of exhibiting malnutrition 10 years later. However, this applied only to individuals with a BMI<25 kg/m2 at the baseline. In conclusion, these findings suggest that preventive actions to counteract malnutrition in older adults should focus on limiting the intake of total fat in the diet by reducing consumption of food with a high content of saturated and monounsaturated fat.

The present study determined the ability of the Mini Nutritional Assessment (MNA) to predict care need in older people. We analysed the datasets of the Taiwan Longitudinal Study on Aging. The 1999 survey containing the MNA items served as the baseline and the 2003 survey served as the endpoint. Of the 4440 participants, 2890 were aged  ≥ 65 years and served as subjects in the present study. After excluding 150 subjects having incomplete data, 2740 were rated for nutritional status with the normalised long-form (LF) and short-form (SF) MNA-Taiwan version 1 (T1) and version 2 (T2) and evaluated with logistic regression analysis for cross-sectional associations of the rated nutritional status with care need, controlled for age, sex, education level, living arrangement and physical activity. Receiver operating characteristic curves were generated for evaluating the ability of the MNA to predict care need. After further excluding 250 subjects who had care need at baseline and seventy-six who were lost to follow-up, 2414 were evaluated for the ability of the MNA to predict subsequent care need with logistic regression analysis. The results demonstrated that all the MNA predicted concurrent and subsequent care need well. The OR for needing subsequent care in the ‘at-risk’ and ‘malnourished’ groups were, respectively, 2·04 and 3·33 for the MNA-T1-LF, 2·10 and 5·35 for the MNA-T2-LF, 1·49 and 2·48 MNA-T1-SF, and 1·80 and 3·44 for the MNA-T2-SF (all P< 0·05), and the respective Nagelkerke R2 values were 0·190, 0·191, 0·184 and 0·192. In conclusion, all the four MNA have the ability to predict future care need, including the MNA-T2-SF, which appears to have great potential for practical applicability.

The aim of the present study was to evaluate the Mini Nutritional Assessment (MNA), the Nutritional Risk Screening (NRS) 2002 and the American Society of Anesthesiologists Physical Status Score (ASA) as predictors of gait status and mortality 6 months after hip fracture. A total of eighty-eight consecutive patients over the age of 65 years with hip fracture admitted to an orthopaedic unit were prospectively evaluated. Within the first 72 h of admission, each patient's characteristics were recorded, and the MNA, the NRS 2002 and the ASA were performed. Gait status and mortality were evaluated 6 months after hip fracture. Of the total patients, two were excluded because of pathological fractures. The remaining eighty-six patients (aged 80·2 (sd 7·3) years) were studied. Among these patients 76·7 % were female, 69·8 % walked with or without support and 12·8 % died 6 months after the fracture. In a multivariate analysis, only the MNA was associated with gait status 6 months after hip fracture (OR 0·773, 95 % CI 0·663, 0·901; P= 0·001). In the Cox regression model, only the MNA was associated with mortality 6 months after hip fracture (hazard ratio 0·869, 95 % CI 0·757, 0·998; P= 0·04). In conclusion, the MNA best predicts gait status and mortality 6 months after hip fracture. These results suggest that the MNA should be included in the clinical stratification of patients with hip fracture to identify and treat malnutrition in order to improve the outcomes.