The study included 380 adults newly diagnosed with histologically confirmed gastric cancer between September 1995 and May 2017. Baseline was defined as the date of first pathological diagnosis, when researchers collected demographic, tumour, treatment, laboratory, nutritional and functional information. The participants had a mean age of 59.5 years, and 65, or 17.1%, were underweight, 220, or 57.9%, were in the normal-weight range, 83, or 21.8%, were overweight, and 12, or 3.2%, were obese. Overall survival was the interval from first histopathological diagnosis to death from any cause, with patients alive at their last documented contact censored. Follow-up ranged from 0.2 to 67.8 months, and the reverse Kaplan–Meier estimate of median follow-up was 37.0 months.

Nutritional and functional differences were pronounced across the BMI categories. Every patient classified as underweight had a Nutritional Risk Screening 2002 score of at least 3, compared with 64.1% of normal-weight patients, 63.9% of overweight patients and 50.0% of obese patients. Severe nutritional impairment, represented by a Patient-Generated Subjective Global Assessment score of at least 9, occurred in 55.4% of underweight patients, 35.5% of normal-weight patients, 28.9% of overweight patients and 8.3% of obese patients. Median Karnofsky Performance Status was 80 in the underweight group and 90 in each of the other groups. Prealbumin concentrations also increased across the categories, while radical surgery was more common among underweight patients. These differences help explain why BMI cannot be interpreted as a biological cause of better survival: it may instead act as a marker of nutritional reserve, disease-related weight loss, functional status and treatment selection.

Kaplan–Meier analysis showed an overall difference in survival among the four BMI categories, with a log-rank p-value of 0.011, and unadjusted survival was lowest in the underweight group. The multivariable model included BMI category, age modelled with a restricted cubic spline, clinical TNM stage, PG-SGA category, Karnofsky Performance Status, prealbumin, neutrophil-to-lymphocyte ratio and baseline chemotherapy, with baseline hazards stratified by surgical procedure and nutritional support. In that model, underweight was not independently associated with overall survival, with a hazard ratio of 0.89 and 95% confidence interval of 0.60–1.34. Advanced disease remained strongly associated with mortality: compared with stage I, the hazard ratio was 4.63 for stage III and 10.93 for stage IV. A 10 mg/L increase in prealbumin was associated with a lower adjusted hazard of 0.972, while receipt of chemotherapy at baseline was associated with a lower hazard of 0.653. The latter variable represented treatment already initiated at baseline and did not specifically identify postoperative adjuvant chemotherapy.

Stage-specific sensitivity analyses suggested different patterns in early and advanced disease, but the formal test did not establish that BMI modified the survival association by stage. In 89 patients with stage I–II disease, including 26 deaths, underweight was associated with a higher mortality hazard than normal weight, with a hazard ratio of 5.19 and a 95% confidence interval of 1.42–18.96. The interval was wide, reflecting the small number of deaths, and neither the overweight nor obesity estimates in the early-stage group were statistically significant. Among 285 patients with stage III–IV disease, including 176 deaths, overweight was associated with lower mortality, with a hazard ratio of 0.61 and 95% confidence interval of 0.41–0.93. Underweight and obesity were not significantly associated with mortality in that advanced-stage subgroup. However, the categorical BMI-by-stage interaction test was not significant, with p=0.173, so the apparently contrasting subgroup results should not be treated as proof that BMI operates differently at each disease stage.

Other nutritional tools also provided partly overlapping information. PG-SGA categories significantly distinguished unadjusted overall survival, with a log-rank p-value of 0.0097, and a score of at least 9 was associated with a higher mortality hazard than a score of 0–3 in the univariable analysis, with a hazard ratio of 1.65. That association was no longer significant after adjustment for stage, BMI, laboratory measures, functional status and treatment variables. NRS2002, by contrast, did not distinguish survival in Kaplan–Meier analysis, with a log-rank p-value of 0.80, and its nutritional-risk category was not associated with mortality in the univariable Cox model. The two instruments were designed for different purposes: NRS2002 screens hospitalized patients for nutritional risk, whereas PG-SGA assesses cancer-related nutrition-impact symptoms, intake, weight change and functional status. In this cohort, about 70% of patients had NRS2002 scores of at least 3, which may have limited its ability to separate patients with different long-term prognoses.

Laboratory and functional measures similarly appeared prognostic before adjustment but were attenuated once disease severity and other correlated factors were included. Better Karnofsky Performance Status, higher albumin and prealbumin, and a lower neutrophil-to-lymphocyte ratio were each associated with lower mortality in univariable analysis, whereas higher neutrophil-to-lymphocyte ratio was associated with increased mortality. In the multivariable model, PG-SGA, Karnofsky Performance Status and neutrophil-to-lymphocyte ratio were not independently associated with overall survival, while prealbumin retained an association. This attenuation is consistent with substantial overlap among nutrition, inflammation, tumour burden and functional reserve. Serum protein concentrations can reflect inflammation, hepatic synthesis and fluid status as well as nutrient intake, and performance status can influence both treatment selection and survival. Restricted cubic spline analysis detected no significant overall or nonlinear association between continuous BMI and mortality, with an overall p-value of 0.640 and a nonlinearity p-value of 0.739.

The investigators also built an exploratory model to estimate survival at one, three and five years. A clinical reference model included age, sex, TNM stage and baseline chemotherapy, while an extended model added continuous BMI, PG-SGA score, Karnofsky Performance Status, prealbumin and neutrophil-to-lymphocyte ratio. The extended model fitted the data better than the clinical model, with a likelihood-ratio chi-square value of 21.80 on five degrees of freedom and p<0.001. After 1,000 bootstrap resamples, the optimism-corrected concordance index rose from 0.646 for the clinical model to 0.681 for the extended model. Time-dependent areas under the curve were 0.744, 0.748 and 0.702 for the extended model at one, three and five years, compared with 0.689, 0.715 and 0.684 for the clinical model. Calibration was generally acceptable at one and three years but more variable at five years. These internal validation results indicate modest incremental discrimination, not proven clinical utility, because the model was not tested in an independent cohort.

Several limitations constrain interpretation of the observational findings and prevent a causal conclusion. The long recruitment period may have included changes in staging, surgery and systemic treatment, and residual confounding from tumour burden, inflammation, comorbidity and treatment selection cannot be excluded. BMI was measured once and cannot distinguish skeletal muscle from adipose tissue, identify visceral adiposity or sarcopenic obesity, or capture recent involuntary weight loss. The obesity group contained only 12 patients, and the early-stage subgroup had just 26 deaths. C-reactive protein, histological differentiation and six-month weight-loss information were available for subsets only; in the 43 patients with six-month weight-loss data, losing at least 5% was associated with higher mortality, but the estimate was exploratory. The prognostic model also requires external validation. The evidence therefore supports BMI of 24.0–27.9 as a prognostic marker associated with lower mortality in this population, while showing that a broader assessment incorporating nutrition, function and inflammation may add limited predictive information. It does not establish that increasing body weight or BMI improves survival.

Subject of Research: Associations between baseline BMI, nutritional status and overall survival in adults with gastric cancer

Article Title: Body mass index and overall survival in patients with gastric cancer: stage-specific associations and prognostic implications

Article References: Zhou, S., Yu, D., Wang, X., Zhan, Z., Guo, Z., & Yu, J. (2026). Body mass index and overall survival in patients with gastric cancer: stage-specific associations and prognostic implications. Frontiers in Nutrition, 13, Article 1937418. https://doi.org/10.3389/fnut.2026.1937418

Image Credits: AI Generated

DOI: 10.3389/fnut.2026.1937418

Keywords: gastric cancer, body mass index, overall survival, nutritional status, PG-SGA, prognostic modeling, observational cohort, mortality risk, Body, mass, index, overall

The study analyzed clinical records from three dialysis centers in Chongqing, China: Daping Hospital, Kaizhou District People’s Hospital and Yongchuan Hospital of Chongqing Medical University. Eligible participants were at least 16 years old, had chronic renal failure and had received regular peritoneal dialysis for more than one month. The source cohort contained 824 records for people who began peritoneal dialysis for the first time between August 2007 and September 2025. After excluding patients with acute kidney injury, emergency dialysis, subsequent renal recovery or kidney transplantation, as well as those lost to follow-up, 737 records remained. Detection of 37 statistical outliers produced a final analytic cohort of 700 patients, who were randomly divided into a training set of 491 and a test set of 209.

During follow-up, 320 patients transitioned to hemodialysis, 178 died and 202 remained on peritoneal dialysis without either event by the end of observation. The investigators defined a hemodialysis transition as a move to hemodialysis lasting more than 90 days without a return to peritoneal dialysis, following a 2026 position statement from the International Society for Peritoneal Dialysis. Patients who died within that 90-day observation window were classified as having died rather than having made a sustained transition. Patients with unknown outcomes at censoring were treated as censored observations, not as competing deaths. Kidney transplantation and renal recovery were excluded from the primary analysis because those categories had few events, which the researchers said could make competing-risk estimates unstable.

Because the investigators expected death to compete with the planned hemodialysis transition, they compared a cause-specific proportional hazards model, two Fine–Gray model-development strategies and a conventional LASSO-Cox model that censored death. The cause-specific model used LASSO feature selection with tenfold cross-validation, while the Fine–Gray models were built using three penalized regression methods—LASSO, smoothly clipped absolute deviation and minimax concave penalty—as well as a boosting procedure. The researchers retained variables identified in univariate screening but also allowed the feature-selection procedures to consider other potentially relevant clinical characteristics. Predictive performance was assessed with time-dependent area under the curve and concordance-index curves, while calibration and discrimination were examined at three, six and nine years.

In the training set, the penalized Fine–Gray model had a mean time-dependent area under the curve of 0.716, compared with 0.696 for the cause-specific model and 0.718 for the boosted Fine–Gray model. Its training-set concordance index was 0.662. The conventional LASSO-Cox model had the highest mean training area under the curve, 0.762, but its performance was weaker in the held-out data: its test-set concordance index was 0.573, compared with 0.646 for the penalized Fine–Gray model. The penalized model also had the highest mean test-set area under the curve at 0.689. The investigators therefore selected it as the best overall model, citing its held-out performance and its smaller predictor set relative to the boosted Fine–Gray model. This was a predictive comparison within one retrospective dataset, not evidence that changing care according to the model would improve patient outcomes.

The final penalized model associated a higher body mass index with a greater subdistribution hazard of hemodialysis transition, with a subdistribution hazard ratio of 1.05 and a 95% confidence interval of 1.00 to 1.09 per BMI unit. A history of hospitalization for peritonitis was associated with more than twice the subdistribution hazard, with a ratio of 2.15 and a confidence interval of 1.64 to 2.84. Better nutrition-related parameters were associated with lower transition hazard, with a ratio of 0.27 and a confidence interval of 0.08 to 0.90. Other significant associations in the penalized model included older age, previous urological surgery and previous hemodialysis. The investigators discussed several plausible mechanisms without proving them in this study: peritonitis may make peritoneal dialysis technically unsustainable or damage the peritoneum; malnutrition may impair immune function and tissue repair; and higher BMI has previously been associated with peritonitis, loss of residual kidney function and hospitalization.

Researchers converted the penalized model into a nomogram that assigns points to clinical variables and maps a patient’s total score to estimated cumulative incidence of hemodialysis transition at three, six and nine years. In a worked example, a 60-year-old man with a BMI of 20.4 kg/m², reduced urine output and previous hospitalization for both peritonitis and cardiovascular disease received approximately 398 points. The nomogram estimated his cumulative transition risk as 39% at three years, 60% at six years and 69% at nine years. Patients in the test cohort were divided into low-, middle- and high-risk groups using score thresholds derived from the training set. Their cumulative incidence curves differed significantly for both hemodialysis transition and death, and the high-risk group had the highest incidence of transition, although a minor early crossing occurred between the low- and middle-risk groups.

Diagnostics also exposed important limitations in the model’s statistical assumptions. The proportional subdistribution hazards assumption was violated for normal 24-hour urine output, hospitalization for peritonitis and the nutrition-related parameters. Stratifying by peritonitis hospitalization did not resolve the violation, while splitting patients at a nutrition-derived threshold produced subgroups that satisfied the assumption. However, the well-nourished subgroup showed substantial differences between training and test performance: its mean area under the curve fell from 0.763 in training to 0.609 in testing, and its concordance index fell from 0.686 to 0.579. The malnutrition subgroup performed more closely between training and testing, with area-under-the-curve values of 0.714 and 0.691 and concordance indices of 0.661 and 0.640. The authors consequently treated the subgroup models as exploratory and retained nutrition as a continuous variable in the main analysis.

The retrospective design, missing laboratory data and lack of time-varying clinical measurements constrain how broadly the results can be applied. Some clinical indicators had missing rates exceeding 30%, requiring mean, nearest-neighbor or multiple-linear-regression imputation that may have introduced bias. The dataset was drawn from three centers in one Chinese city, and the paper’s limitations section describes the work as single-center despite the multicenter data sources, an internal inconsistency that further emphasizes the need for independent validation. The model also used baseline information rather than changes in nutrition, infection or laboratory status during follow-up, and it did not establish that risk prediction led to better clinical decisions or outcomes. The authors proposed prospective multicenter validation and models capable of incorporating dynamic covariates. Until those steps are completed, the penalized Fine–Gray model and nomogram are best regarded as retrospective risk-estimation tools whose discrimination is moderate, with meaningful uncertainty around their individual predictions.

Subject of Research: Competing-risk prediction of peritoneal dialysis-to-hemodialysis transition in adults

Article Title: Optimal prediction of peritoneal dialysis to hemodialysis transition: a penalized fine-gray model

Article References: Wan, J., Wei, Q., Wu, D., Hou, S., Yv, K., Tang, R., He, J., Chen, X., Li, Y., Hu, Y., Yang, J., Li, T., & Chen, K. (2026). Optimal prediction of peritoneal dialysis to hemodialysis transition: a penalized fine-gray model. Frontiers in Medicine, 13, Article 1843381. https://doi.org/10.3389/fmed.2026.1843381

Image Credits: AI Generated

DOI: 10.3389/fmed.2026.1843381

Keywords: peritoneal dialysis, hemodialysis transition, competing risks, Fine–Gray model, clinical prediction, nomogram, peritonitis, nutritional status, retrospective cohort, model validation, Optimal, prediction

The flies, whose scientific name is Scathophaga stercoraria, were randomly assigned to three nutritional regimes and kept in a laboratory climate chamber for at least 24 and up to 48 hours. One group received water only, a second received water and sugar as a substitute for nectar, and a third received water, sugar and small Drosophila prey. The supplemented flies were held at approximately 20 degrees Celsius before being returned to the pasture. From early morning until late evening, as many as five observers searched the pasture and freshly deposited dung pats for individually marked males, recording each fly’s identity, pairing status and time of observation.

Of the 540 males released, 231, or approximately 43%, were resighted at least once. Forty-six of the resighted flies were seen only once at a single dung pat, while 114 occupied one pat for longer and 117 switched pats at least once. The researchers used those observations to estimate mating success, the distance from one pat to the next, the number of sightings per day, the total number of pats visited, and the time between a male’s first and last appearance. These measures allowed laboratory feeding conditions to be linked with individual behavior in the field, an approach that is unusually difficult for small insects.

The experiment recorded 89 copulations, with 59 males copulating once, eight twice, two three times and two four times. The proportion of resighted males seen copulating was 0.22 in the water-only treatment, 0.35 in the sugar treatment and 0.40 in the sugar-plus-prey treatment. These values pointed toward greater mating success among supplemented males, but the treatment effect was not statistically significant, partly because relatively few copulations were observed. The average number of flies resighted also tended to be lower in the water group, with 10 per 30 released, compared with 15.5 in the sugar group and 13 in the sugar-plus-prey group, although the treatment differences did not reach the stated significance threshold.

Initial body mass was positively related to mating success regardless of feeding treatment. Larger males also traveled greater estimated distances when moving to their next pat and showed a trend toward visiting more pats. Their minimum residence time at a pat generally declined with body size, apart from males in the two supplemented groups, a pattern interpreted as evidence of greater energetic reserves and activity. No body-mass-by-treatment interaction was significant for any measured variable, meaning the data did not support the prediction that extra nutrients would help large males disproportionately or that smaller males would gain a mating advantage when energy was limited.

Travel distance was the one behavioral variable significantly affected by nutrition. Males receiving sugar alone moved a mean estimated 28.19 meters to the next pat, and those receiving sugar plus prey moved 23.90 meters, compared with 16.41 meters for males given only water. The number of pats visited, number of times seen during the day and total active time did not differ significantly among the feeding treatments. Mean active time was 129.1 minutes for water-fed males, 182.3 minutes for sugar-fed males and 146.9 minutes for males fed sugar and prey. Initial body mass, as expected from random assignment, also did not differ significantly among treatments.

The field setting addressed a distinctive feature of yellow dung fly reproduction. Adult flies use nectar for energy and insect prey to support egg and sperm production. Females spend much of their time foraging away from dung and visit a pat when ready to lay eggs. Males are more often found waiting on or around fresh dung, attempting to mate with arriving females and guard them during oviposition, although intense competition can make defense of a pat impractical. A male therefore has to choose repeatedly between remaining where females may arrive and leaving to replenish energy or sperm. Those decisions may depend on body size, nutritional reserves, sperm availability, and the local densities of rivals and potential mates.

Sugar and prey were expected to have different functions. Sugars fuel flight and fighting, while proteins obtained from insect prey can support sperm production; yellow dung flies cannot convert sugar into protein. The researchers also reasoned that larger males might benefit more from supplementation because they have greater absolute requirements for energy and sperm. The observed differences were limited: adding Drosophila prey did not yield a clear advantage over sugar alone in copulation rate, and no significant body-mass-by-treatment interaction was detected. The nutritional treatments may have overlapped in practice because the author reported that yellow dung flies can obtain sugar from prey and can be maintained in the laboratory on prey without a separate sugar source.

Sperm reserves offer another possible explanation for the limited difference between diets. Male sperm is initially produced after adult emergence when prey is available, a process that takes about three to seven days, and production may continue during adult life. Stored sperm can support approximately four to five copulations. Consequently, many sugar-fed males may still have retained enough sperm for mating after capture even though they received no insect prey during the 24-to-48-hour treatment. The experiment therefore tested the effects of short-term supplementation on a population whose age and initial nutritional and reproductive condition were not known, rather than creating males with precisely controlled reserves from emergence onward.

Several limitations restrict the conclusions. The adult ages of the captured males were not estimated because the proposed wing-based age-grading method required wing removal and digital analysis, leaving age as an uncontrolled influence on flight performance, searching and reproductive condition. The observations covered only six days: three in spring and three in autumn across two years. Fly density and body size can change substantially within a season, and at least four generations overlap annually in Switzerland. Daily weather also varied strongly among dates, and the day effect was prominent in the statistical analysis. Marked males also disappeared quickly from the focal pasture, and emigration could not be distinguished from death. These factors lowered effective sample sizes and probably contributed to the lack of statistical significance for the key mating comparison.

Despite those constraints, the mark-resight design showed that individual behavior and reproductive outcomes can be measured in a small field insect. The clearest measured nutritional effect was longer movement between dung pats, not a demonstrated rise in copulation success. Body size remained positively associated with mating success, and the expected interaction between size and nutrition did not emerge. The study therefore offers a bounded lesson about the energetic economics of mating: readily available sugar is associated with greater travel capacity, but in this experiment it did not reliably translate into more observed copulations, and adding insect prey produced no clear additional mating benefit over sugar alone.

Subject of Research: Field experiment on how nutrition and body size affect male yellow dung fly movement and mating success.

Article Title: Energetic underpinnings of yellow dung fly mating success in the field

Article References: Blanckenhorn, W. U. (2021). Energetic underpinnings of yellow dung fly mating success in the field. Alpine Entomology, 5, 61-67. https://doi.org/10.3897/alpento.5.68153

Image Credits: AI Generated

DOI: 10.3897/alpento.5.68153

Keywords: yellow dung fly, mating success, nutritional status, body size, field experiment, mark-resight method, foraging trade-offs, insect behavior, dung pats, sexual selection, Energetic, underpinnings

The study followed consecutive adults who presented to the emergency department with new or worsening signs and symptoms compatible with acute heart failure between March 2022 and March 2026. Acute heart failure was confirmed retrospectively using clinical findings plus at least one objective indicator available by the index assessment, such as an age-adjusted NT-proBNP rule-in threshold, compatible echocardiographic abnormalities, or imaging showing pulmonary vascular congestion or pulmonary edema. The analysis included both new-onset disease and acute decompensation of chronic heart failure and did not require a particular left ventricular ejection fraction. Patients were excluded if, among other reasons, severe infection, active malignancy, major hepatic failure, a relevant blood disorder, albumin infusion, or immunosuppressive therapy could substantially distort the nutrition-related laboratory values.

PNI was calculated as serum albumin in grams per liter plus five times the peripheral lymphocyte count in units of 10⁹ per liter, using the first eligible measurements obtained within 24 hours of admission. The index time was the later collection time of the two specimens. Investigators used a prespecified literature-derived threshold to divide the cohort into low-PNI and high-PNI groups. This approach exposed an important limitation of interpreting the score as a direct nutritional assessment: serum albumin and lymphocytes can change with inflammation, congestion, fluid expansion, physiological stress, organ dysfunction, and medication exposure as well as with nutritional reserve. The low-PNI group was therefore already more vulnerable across several dimensions at the time of assessment.

Those differences were substantial. Low-PNI patients were older, had a lower body mass index, and were more likely to have chronic kidney disease or previous hospitalization for heart failure. At the index assessment, they had lower systolic blood pressure and oxygen saturation, faster heart and respiratory rates, more advanced Killip and New York Heart Association classes, and more frequent hypotension, hypoperfusion, and ongoing advanced support. Their laboratory profile also suggested greater inflammatory, cardiorenal, and cardiac stress: median C-reactive protein was 22.6 mg/L versus 9.8 mg/L, median NT-proBNP was 7,912 pg/mL versus 5,305 pg/mL, mean estimated glomerular filtration rate was 54.7 versus 68.1 mL/min/1.73 m², and mean high-sensitivity cardiac troponin I was 57 versus 35 ng/L. Echocardiography and chest imaging likewise showed lower mean ejection fraction, more right ventricular dysfunction, and more pulmonary congestion, edema, and pleural effusion in the low-PNI group.

Events were counted only if they occurred after the index PNI assessment and before discharge or in-hospital death. The primary composite comprised all-cause death, cardiogenic shock, sustained ventricular tachycardia or ventricular fibrillation, cardiac arrest requiring cardiopulmonary resuscitation, acute myocardial infarction, or worsening acute heart failure requiring treatment escalation. Overall, 112 patients, or 30.5%, experienced at least one qualifying event. The low-PNI group had higher rates of all-cause death (14.7% versus 5.7%), cardiogenic shock (18.6% versus 8.5%), sustained ventricular tachycardia or fibrillation (9.6% versus 3.8%), cardiac arrest (7.7% versus 2.8%), and worsening acute heart failure requiring escalation (32.7% versus 15.2%). Acute myocardial infarction did not differ significantly between groups, at 6.4% versus 3.3%. Low-PNI patients were also more often admitted to intensive care, ventilated, or given vasoactive drugs after assessment, and their median hospital stay was 10 days rather than 8.

The association also showed a graded pattern when PNI was treated as a continuous exposure. In the fully adjusted model, each five-point increase was associated with 30% lower odds of the composite endpoint, with an odds ratio of 0.70 and 95% confidence interval of 0.56–0.90; P = 0.003. The estimate for a 10-point increase was 0.49, with a 95% confidence interval of 0.31–0.82. Patients in the lowest PNI quartile had 2.57 times the adjusted odds of the primary outcome compared with those in the highest quartile, and each quartile increase was associated with lower odds, with an odds ratio of 0.72; P for trend = 0.004. The relationship remained in a complete-case analysis and when patients with a component event already present at assessment were excluded.

PNI alone had moderate discrimination, producing an apparent area under the receiver operating characteristic curve of 0.714, with 95% confidence interval 0.658–0.771. At the exploratory cohort-derived cutoff of 44.8, sensitivity was 60.7% and specificity 65.5%, showing that the score could not accurately classify individuals on its own. Its value was more apparent when added to a conventional model containing established clinical information. The conventional model achieved an AUC of 0.776, while the model with continuous PNI reached 0.829, an increase of 0.053 with a 95% confidence interval of 0.021–0.085; P = 0.001. Bootstrap correction reduced the corresponding AUCs to 0.752 and 0.803. The combined model also had a lower corrected Brier score, 0.151 versus 0.174, while its corrected calibration slope changed modestly from 0.86 to 0.88.

These results do not show that PNI causes deterioration or that changing the index would prevent cardiovascular events. The design was observational, based at one center, and vulnerable to selection bias, residual confounding, and inaccuracies in medical records. PNI was calculated from a single measurement, formal nutritional and frailty assessments were unavailable, and serial changes could not be examined. Multiple imputation addressed missing covariates under an unverifiable missing-at-random assumption. The composite also combined events of different severity, and treatment escalation can be influenced by clinical judgment and institutional practice. Nevertheless, a sensitivity analysis using a “hard” composite that excluded worsening heart failure and resource-use outcomes produced a similar adjusted association. After excluding patients with C-reactive protein of at least 50 mg/L, the estimate remained directionally consistent but became less precise, with an adjusted odds ratio of 1.79 and a 95% confidence interval of 0.98–3.26; P = 0.058.

The authors describe PNI as a potentially complementary early marker rather than a stand-alone triage rule or therapeutic target. Its ingredients are available from routine laboratory testing, making the calculation practical in emergency and inpatient care, but the observed model improvement does not itself demonstrate better clinical decisions or improved patient outcomes. Exploratory subgroup analyses found no statistically detectable variation in the association by age, sex, ejection-fraction category, diabetes, chronic kidney disease, NT-proBNP level, or atrial fibrillation; the interaction P-values were all at least 0.742. Some subgroup confidence intervals included 1.00, however, so the analysis indicates no detected effect modification rather than proving identical relationships across phenotypes. Prospective multicenter studies with external validation, longer follow-up, serial measurements, and formal nutritional, inflammatory, congestion, and frailty assessments are needed before PNI-guided management can be recommended.

Subject of Research: Association of early prognostic nutritional index with in-hospital adverse cardiovascular events in adults with acute heart failure

Article Title: Association of the prognostic nutritional index with in-hospital adverse cardiovascular events among emergency department patients with acute heart failure

Article References: Zhang, X.-P., Guo, W.-W., & Li, B. (2026). Association of the prognostic nutritional index with in-hospital adverse cardiovascular events among emergency department patients with acute heart failure. Frontiers in Nutrition, 13, Article 1949478. https://doi.org/10.3389/fnut.2026.1949478

Image Credits: AI Generated

DOI: 10.3389/fnut.2026.1949478

Keywords: acute heart failure, prognostic nutritional index, emergency medicine, in-hospital outcomes, nutritional status, risk stratification, albumin, lymphocyte count, cardiovascular events, retrospective cohort, Association, prognostic

Acute pulmonary embolism remains one of the most lethal cardiovascular emergencies, occurring when a thrombus, usually dislodged from a deep vein in the legs, travels through the venous system and lodges in the pulmonary arteries. Despite major advances in computed tomography pulmonary angiography, anticoagulation, and thrombolytic therapy, mortality remains substantial, and in this cohort it was striking: 102 of the 471 patients—21.7 percent—died within a single year of diagnosis. Conventional risk stratification relies on clinical scoring systems such as the Pulmonary Embolism Severity Index and its simplified variant, the sPESI, which integrate age, vital signs, comorbidities, and hemodynamic status. Yet these scores were never designed to capture the inflammatory and immunological processes that increasingly appear to shape long-term outcomes after the acute event has been treated.

The CALLY index attempts to do precisely that. It is calculated as the product of serum albumin in grams per deciliter and the absolute lymphocyte count in thousands per microliter, divided by the C-reactive protein concentration in milligrams per deciliter. Each component carries distinct biological information. C-reactive protein, synthesized by hepatocytes under the influence of the proinflammatory cytokines interleukin-1 and interleukin-6, is one of the most sensitive and standardized markers of the acute-phase response, reflecting endothelial dysfunction and heightened thrombotic tendency. Lymphocytes, by contrast, are central to immune homeostasis, and low counts signal impaired immune function and systemic stress. Albumin is far more than a nutritional marker: it exerts anti-inflammatory, antioxidant, and antithrombotic effects, and low levels indicate a catabolic state, hypercoagulability, and depleted physiological reserve. By multiplying the protective components and dividing by the harmful one, the index collapses three biological domains into a single number.

The retrospective design excluded patients with severe active infections, systemic inflammatory or autoimmune disease, or advanced liver failure, and restricted the malignancy exclusion to those with active hematological disease or terminal-stage solid tumors. After screening 536 patients, the final analysis set comprised 471 individuals whose admission laboratory panels, echocardiographic measurements, and follow-up data were extracted from electronic medical records. Patients who died within the year were significantly older than survivors, with a mean age of 69.2 versus 63.8 years, and were far more likely to have malignancy, which affected 32.4 percent of non-survivors compared with 12.2 percent of survivors, and immobility, present in 33.3 percent versus 14.1 percent.

The laboratory differences between groups were equally pronounced. Non-survivors had markedly higher C-reactive protein concentrations, averaging 5.94 milligrams per deciliter versus 2.93 in survivors, along with elevated neutrophil and white blood cell counts, higher D-dimer and troponin levels, and significantly lower lymphocyte counts, hemoglobin, hematocrit, albumin, and total protein. Echocardiography revealed greater right ventricular overload in those who died, with higher systolic pulmonary artery pressure, larger right ventricular diameter, and larger left atrial diameter. Median CALLY values told the story in a single number: 0.5 in non-survivors versus 3.4 in survivors, a difference so stark it held under the strictest statistical threshold.

To quantify how well each index discriminated between those who lived and died, the researchers performed receiver operating characteristic analysis. The CALLY index achieved an area under the curve of 0.787, with a 95 percent confidence interval of 0.732 to 0.842, significantly exceeding the PNI at 0.728 and the SII at 0.643, with pairwise DeLong tests confirming the superiority of CALLY over both competitors. The optimal cutoff, derived using the Youden index, was a CALLY value of 1.6. The clinical consequences of falling on either side of that threshold were dramatic: one-year mortality was 40.8 percent among patients with CALLY values at or below 1.6, compared with just 9.9 percent among those above it. Thirty-day mortality followed the same pattern, at 14.5 percent versus 3.8 percent, while hospital length of stay did not differ, hinting that the index captures long-term biological vulnerability rather than the mechanics of the acute admission.

Cox regression analysis confirmed that these were not statistical artifacts. When CALLY was entered as a continuous variable into multivariable models adjusted for age, an sPESI score of at least one, elevated cardiac troponin, and systolic pulmonary artery pressure, each one-unit increase in CALLY was associated with a hazard ratio of 0.904 for one-year mortality—a 10 percent reduction in risk per unit—highly significant even after adjustment. Separate models constructed for PNI and SII, to avoid multicollinearity from their shared components, showed that both indices were also independently associated with mortality, with higher SII values carrying a hazard ratio of 1.020 per 100-unit increase. Notably, in a sensitivity analysis restricted to 385 patients without malignancy, the CALLY association persisted virtually unchanged, with an adjusted hazard ratio of 0.899, indicating that the result was not simply driven by the cancer cases clustered among the deaths.

Perhaps most compelling is the demonstration of incremental value. The researchers built a baseline prognostic model containing the established predictors—age, sPESI of at least one, elevated troponin, and pulmonary artery pressure—and then added CALLY. The C-statistic, a measure of discrimination, rose from 0.700 to 0.739, and the likelihood-ratio test confirmed a highly significant improvement in model fit. In practical terms, a number that costs nothing to obtain, since C-reactive protein, albumin, and a differential blood count are measured routinely in nearly every hospitalized patient, added genuine prognostic information beyond what careful clinical assessment already provides. Kaplan-Meier survival curves reinforced the point, showing significantly worse one-year survival for patients below the CALLY and PNI cutoffs and above the SII threshold of 996.

The logic behind CALLY’s superiority over pure inflammation markers such as SII is that long-term survival after pulmonary embolism depends on more than the initial thrombotic insult. Inflammatory activation, right ventricular overload, endothelial injury, and persistent thrombotic processes interact with malnutrition, immune dysfunction, and reduced physiological reserve, particularly in older patients with multiple comorbidities. An index that simultaneously reads inflammation, immunity, and nutritional status captures a broader slice of that biology than any single measure. Prior studies support the concept: low CALLY values have independently predicted poor outcomes in heart failure, acute coronary syndromes, and patients undergoing transcatheter aortic valve implantation, and the index was originally developed as a prognostic tool in hepatocellular carcinoma surgery.

The authors are careful to note the limitations. The study was retrospective and single-center, temporal changes in the indices during follow-up could not be assessed because only admission values were used, and the endpoint was all-cause rather than cause-specific mortality. Residual confounding cannot be excluded in an observational design, and the 1.6 cutoff was derived and tested within the same cohort, which tends to produce optimistic performance estimates; the threshold therefore requires validation in an independent external population before it can guide clinical decisions. Even so, the message is striking in its simplicity. Three numbers that already sit in every admission chart, combined by a single division, may help clinicians identify which pulmonary embolism patients carry hidden biological fragility—and direct intensified surveillance and follow-up toward those who need it most.

Subject of Research: Prognostic value of the CALLY index for one-year mortality in acute pulmonary embolism

Article Title: Prognostic value of the C-reactive protein–albumin–lymphocyte index for predicting one-year mortality in acute pulmonary embolism

Article References: Kara, A., Erdoğan, G., Sivri, S., Yenerçağ, M., Boyacı, F., Şeker, O. O., Karagöz, A., Öztürk, E. F., & Aşcı, S. M. (2026). Prognostic value of the C-reactive protein–albumin–lymphocyte index for predicting one-year mortality in acute pulmonary embolism. Frontiers in Cardiovascular Medicine, 13, Article 1928531. https://doi.org/10.3389/fcvm.2026.1928531

Image Credits: AI Generated

DOI: 10.3389/fcvm.2026.1928531

Keywords: acute pulmonary embolism, CALLY index, C-reactive protein, albumin, lymphocyte count, one-year mortality, risk stratification, inflammation, nutritional status, Prognostic Nutritional Index, Systemic Immune-Inflammation Index, biomarkers

The research team, led by Ayla Yildiz of the Department of Medical Biochemistry at Başakşehir Çam and Sakura City Hospital in Istanbul, together with Binnur Karabiyik from the hospital’s psychiatry department and Levent Deniz from the University of Health Sciences Türkiye Istanbul Training and Research Hospital, set out to answer a question that had not previously been addressed in the literature: whether indices derived from standard blood counts differ between psychiatric patients with documented drug exposure and people without psychiatric illness. Because these indices require nothing more than a complete blood count and, in one case, routine biochemistry, they have attracted growing interest across medicine as accessible proxies for systemic inflammation and nutritional status.

The three indices at the heart of the study each capture a different facet of immune balance. The systemic inflammation response index, or SIRI, is computed from the counts of monocytes, neutrophils, and lymphocytes, and is interpreted as a reflection of the innate inflammatory response. The systemic immune-inflammation index, or SII, multiplies the platelet count by the neutrophil-to-lymphocyte ratio, combining clues about clotting activity and cellular immune balance into a single figure. The prognostic nutritional index, or PNI, blends the absolute lymphocyte count with serum albumin concentration, and is conventionally used as a measure of immunonutritional reserve. Elevated values on the first two indices are generally read as signs of heightened systemic inflammation, whereas lower PNI values point toward depleted nutritional and immune reserves.

To test whether these markers behave differently in the presence of psychiatric illness and drug use, the investigators assembled three groups. The largest comprised 760 psychiatric inpatients whose urinary drug metabolite screening was positive. A second group consisted of 171 psychiatric patients without positive metabolites, and the third included 151 healthy controls. Patients with infectious, autoimmune, or malignant diseases, or with incomplete data, were excluded, a step designed to prevent conditions known to distort inflammatory and nutritional markers from confounding the comparison. All indices were calculated from hematological and, where relevant, biochemical parameters collected as part of routine care.

The headline result was a clear divergence on two of the three measures. Patients carried a median SIRI of 1.24, with an interquartile range of 0.79 to 1.97, compared with 0.98, ranging from 0.74 to 1.27, in controls. Their mean PNI was 56.0, with a standard deviation of 6.12, against 58.8, with a standard deviation of 3.87, in the healthy group. Both differences were statistically significant at p less than 0.001. The SII, by contrast, did not differ significantly between patients and controls, with a p value of 0.108, indicating that the combined platelet and neutrophil-lymphocyte signal was not the discriminating variable in this population.

Regression analyses reinforced the pattern. After adjusting for age and sex, SIRI remained positively associated with the presence of psychiatric disease, with an odds ratio of 1.847 and a 95 percent confidence interval spanning 1.423 to 2.398, meaning each unit increase in the index was tied to nearly a doubling of the odds of belonging to the patient group. PNI moved in the opposite direction, showing an inverse association with disease presence, with an odds ratio of 0.917 and a 95 percent confidence interval of 0.889 to 0.947. In other words, higher inflammation readings and lower nutritional reserve scores both tracked with psychiatric illness in this cohort.

Receiver operating characteristic analysis, which evaluates how well a marker separates two groups across all possible cutoffs, added a diagnostic dimension. In the subgroup of drug-positive patients with schizophrenia, the area under the curve reached 0.727 for SIRI and 0.804 for PNI. An area under the curve of 0.5 indicates performance no better than chance, while 1.0 indicates perfect separation, so values in the 0.7 to 0.8 range are generally considered moderately discriminative. The PNI figure in particular suggests that the combined lymphocyte-albumin score carried meaningful information about which individuals belonged to the drug-positive schizophrenia subgroup in this sample.

Yet the study’s most instructive findings may be the ones that did not survive scrutiny. Once the researchers adjusted for age and sex and applied corrections for multiple comparisons, no significant drug-status-related differences in SIRI or SII remained within the diagnostic subgroups. The one signal that persisted was a lower PNI among drug-positive patients with nonorganic psychosis. This pattern of attenuation matters because it highlights how easily raw group differences in inflammatory markers can reflect demographic composition rather than biology, and it underscores the need for careful statistical control in biomarker studies of psychiatric populations.

The authors also observed that substance-specific associations varied according to both psychiatric diagnosis and the type of substance involved, meaning that the relationship between drug exposure and immune-nutritional markers was not a one-size-fits-all phenomenon. Urinary drug metabolite screening in the study covered substances such as tetrahydrocannabinol, the principal psychoactive component of cannabis, and amphetamine-class compounds, and the interplay between a given substance and a given diagnosis appeared to shape the biomarker profile. This heterogeneity cautions against interpreting any single index as a universal signature of drug-related immune dysregulation in psychiatry.

Taken together, the study establishes that differences in SIRI and PNI exist between psychiatric patients and healthy controls, but it equally demonstrates that these differences are inconsistent across diagnostic and substance-use subgroups and that some associations weaken substantially after adjustment for age and sex. The researchers conclude that the variability in inflammatory and immunonutritional indices across psychiatric and substance-use subgroups warrants further investigation. For now, the work positions routine blood-derived indices as promising, inexpensive candidates for monitoring immune and nutritional status in psychiatric care, while making clear that their clinical value will depend on understanding how diagnosis, substance type, and demographic factors jointly shape what these numbers mean.

Subject of Research: Inflammatory and nutritional blood indices in psychiatric patients with positive urinary drug metabolites

Article Title: SIRI, SII, and PNI in psychiatric patients with positive urinary drug metabolites: association with immune and nutritional biomarkers

Article References: Yildiz, A., Karabiyik, B., & Deniz, L. (2026). SIRI, SII, and PNI in psychiatric patients with positive urinary drug metabolites: association with immune and nutritional biomarkers. BMC Psychiatry, 26(1), Article 698. https://doi.org/10.1186/s12888-026-08587-9

Image Credits: AI Generated

DOI: 10.1186/s12888-026-08587-9

Keywords: SIRI, SII, PNI, psychiatry, drug metabolites, inflammation, biomarkers, schizophrenia, substance use, nutritional status, hematology, retrospective study

The logic behind the index is deceptively simple. C-reactive protein, an acute-phase protein produced by the liver, rises rapidly in response to systemic inflammation and is one of the most widely used markers of inflammatory activity in clinical practice. Ferritin, the intracellular iron-storage protein, is also an acute-phase reactant, and in severe COVID-19 it can reach extraordinary concentrations, partly reflecting macrophage activation and the cytokine-driven disarray that characterizes critical disease. Albumin, by contrast, falls during inflammation and malnutrition, a phenomenon known as transient capillary leak combined with suppressed hepatic synthesis and, in prolonged illness, genuine protein-energy depletion. By placing the two inflammatory markers in the numerator and the nutritional marker in the denominator, the HNI is intended to capture, in a single value, the simultaneous burden of inflammation and the erosion of nutritional reserve.

The study itself was an observational cross-sectional analysis of 60 patients with confirmed COVID-19, stratified into three groups according to the level of care they required: outpatients treated without hospital admission, patients managed on general wards, and patients admitted to the intensive care unit. The researchers measured a broad panel of laboratory parameters, including standard inflammatory markers, a set of cytokines, and β2-microglobulin, a small protein component of the major histocompatibility complex class I molecule that is shed into the serum during heightened immune cell turnover. The retrospective design relied on stored serum samples collected during routine clinical practice, and the protocol received ethics approval from the Sakarya University Faculty of Medicine Health Sciences Scientific Research Ethics Committee, with the informed consent requirement waived because only archived specimens were used.

The central finding was a clear gradient across the three care levels. Intensive care patients exhibited significantly higher concentrations of inflammatory markers, cytokines, β2-microglobulin, and the HNI itself compared with ward patients and outpatients. This pattern is consistent with the established understanding of severe COVID-19 as a state of escalating immune activation, in which circulating interleukin-6, interleukin-10, and tumor necrosis factor-alpha rise in parallel with acute-phase proteins, and in which rapid lymphocyte turnover releases β2-microglobulin into the circulation. The fact that a composite index derived from three routine laboratory tests tracked this gradient as faithfully as the more specialized cytokine assays is the study’s most practically interesting observation.

The correlation analysis sharpened that picture considerably. HNI showed a very strong correlation with ferritin, with a correlation coefficient of 0.945, a figure so high that it raises an immediate statistical question: when a variable is part of the formula that defines an index, the two will inevitably move together, a phenomenon known as mathematical coupling. The authors themselves flag this concern explicitly, noting that the strong association between HNI and ferritin should be interpreted cautiously precisely because ferritin sits in the numerator of the index. Beyond ferritin, HNI showed moderate correlations with the measured cytokines, suggesting that the index does carry information about the broader inflammatory milieu rather than merely echoing a single component of its own formula.

Diagnostic performance was assessed using receiver operating characteristic analysis, a standard technique for evaluating how well a continuous marker discriminates between two groups, in this case presumably severe and non-severe disease. The area under the curve for HNI reached 0.953, a value that, on its face, would indicate near-perfect discrimination. An AUC of 1.0 represents flawless classification and 0.5 represents performance no better than chance, so 0.953 would place HNI among the most accurate severity markers reported for COVID-19. Yet the authors are careful to temper enthusiasm, emphasizing that this finding should be interpreted cautiously given the small sample size of 60 patients and the potential for mathematical coupling between the index and its constituent measurements.

That caution is well founded and reflects a broader lesson in biomarker research. Composite indices derived from routine laboratory values have proliferated during and after the pandemic, ranging from the neutrophil-to-lymphocyte ratio to more elaborate immunonutritional scores combining multiple cell counts and serum proteins. Their appeal is obvious: they are cheap, they require no specialized assays, and they can be calculated from blood tests already ordered for clinical reasons. Their peril is equally obvious: combining correlated variables can inflate apparent performance, small samples can produce optimistic estimates that shrink dramatically on validation, and the arithmetic of an index can create associations that do not exist in any single underlying measurement. The authors of the present study appear acutely aware of these pitfalls, describing HNI as an exploratory marker rather than a validated clinical tool.

Perhaps the most intellectually honest element of the paper is its conclusion about what HNI actually represents. Rather than claiming the index as an independent biomarker with novel predictive power, the authors conclude that it appears to reflect the combined burden of inflammation and nutritional status, and that its clinical utility beyond the individual parameters from which it is built remains uncertain. This framing matters. A composite index earns its place in clinical practice only if it adds information that its components do not already provide, either by improving risk stratification, by simplifying decision-making, or by capturing an interaction between inflammation and nutrition that neither dimension reveals alone. Whether HNI meets any of those tests cannot be determined from a 60-patient cross-sectional study; it requires validation in larger, ideally prospective cohorts with outcomes such as mortality, mechanical ventilation, and length of stay as endpoints.

The inclusion of β2-microglobulin in the analysis adds a distinctive dimension to the work. As a component of MHC class I molecules expressed on nearly all nucleated cells, β2-microglobulin is released during increased cellular turnover and immune activation, and elevated serum levels have been reported in severe infections, in autoimmune disease, and in conditions involving lymphocyte proliferation. Its significant elevation in the intensive care group of this COVID-19 cohort aligns with the picture of profound immune activation in critical disease, and its correlation with the composite index suggests that HNI, despite its simple formula, moves in step with markers of deep immunological disturbance. Whether β2-microglobulin itself offers prognostic value beyond established markers remains a question for larger studies, but its presence here underscores the study’s attempt to connect a bedside index to the underlying biology of the cytokine-driven inflammatory response.

The study, led by Gülsüm Kaya of Yalova University with colleagues including corresponding author Cengiz Karacaer of Sakarya University, was conducted without external funding and published as open access research on 29 September 2026. Its contribution is best understood as a carefully hedged proof of concept: evidence that a single arithmetic combination of C-reactive protein, ferritin, and albumin can track the severity gradient of COVID-19 and correlate with both cytokine levels and β2-microglobulin, offered with explicit acknowledgment of the mathematical and statistical caveats that such composite measures demand. For clinicians, the message is that routine laboratory data already contain much of the severity signal that specialized assays reveal, but that no new index should be adopted into practice until it has demonstrated, in adequately powered validation studies, that it adds genuine value beyond the numbers already on the chart. For researchers, the work provides a template for how composite biomarkers should be evaluated: with correlation analyses that acknowledge coupling, with ROC analyses interpreted in light of sample size, and with conclusions that distinguish what an index measures from what it independently predicts.

Subject of Research: A combined inflammatory and nutritional biomarker index for assessing COVID-19 severity

Article Title: Hyperinflammatory nutritional index as an exploratory combined inflammatory and nutritional marker: associations with cytokines and β2-microglobulin in COVID-19

Article References: Kaya, G., Karacaer, C., Sunu, C., Ergenç, H., & Öğütlü, A. (2026). Hyperinflammatory nutritional index as an exploratory combined inflammatory and nutritional marker: associations with cytokines and β2-microglobulin in COVID-19. BMC Infectious Diseases, 26(1), Article 1738. https://doi.org/10.1186/s12879-026-14525-7

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14525-7

Keywords: COVID-19, hyperinflammatory nutritional index, C-reactive protein, ferritin, albumin, cytokines, β2-microglobulin, inflammation, nutritional status, biomarkers, disease severity, immunonutrition

The research team, led by Gül Eda Kılınç Tulum together with Yasemin Açar Kuru and Aliye Özenoğlu, set out with three linked goals: to characterize the feeding-related behaviors of children and adolescents with autism, to identify feeding-related factors from the perspective of caregivers, and to examine how nutritional status, quality of life, and behavioral problems relate to one another. Rather than relying on a single measure, the investigators assembled a battery of validated instruments, each targeting a different layer of the mealtime experience. The Brief Autism Mealtime Behavior Inventory, known as BAMBI, captured child mealtime behaviors; the Screening Tool of Feeding Problems, or STEP, flagged feeding difficulties; the Feeding Strategies Questionnaire documented parental approaches; the Quality of Life in Autism questionnaire, QoLA, assessed caregiver well-being; and a food frequency questionnaire charted dietary intake patterns.

The sample itself reflects the demographic profile often seen in pediatric autism research. The children and adolescents had a mean age of 8.98 years, with a standard deviation of 3.86, and three-quarters of them, 75.0 percent, were male. Perhaps more striking was the anthropometric finding: 33.3 percent of the participants were classified as overweight or obese according to World Health Organization growth references. This figure echoes a growing body of meta-analytic evidence showing that children with autism face elevated risks of excess weight gain, a pattern that may seem paradoxical in a population famous for picky eating but is consistent with diets dominated by energy-dense, highly processed, and palatable foods that survive even the strictest food selection filters.

On the BAMBI, the mean total score was 40.25 with a standard deviation of 8.24, and the sub-dimension analysis revealed where the trouble concentrates. The limited variety subscale scored highest at 20.6, with a standard deviation of 5.87, indicating that restricted food variety was the single most prominent feeding concern in this cohort. Food selectivity in autism is well documented in the broader literature; systematic reviews and large clinical cohorts have repeatedly found that children with autism accept fewer foods, reject entire food groups, and demand ritualized presentation far more often than their neurotypical peers. What the new study adds is a quantified hierarchy: within this sample, the narrowness of the accepted diet outweighed other mealtime behaviors such as food refusal or disruptive conduct at the table.

The STEP results told a complementary story. Among its sub-dimensions, nutrition-related behavioral problems achieved the highest mean score, 3.27 with a standard deviation of 3.12. This category encompasses difficulties that directly interfere with adequate nourishment, from refusing entire texture classes to vomiting or distress around specific nutrients and food types. Meanwhile, the Feeding Strategies Questionnaire revealed that the highest-scoring parental approach fell under child control of intake, with a mean of 19.33 and a standard deviation of 6.14. In practical terms, parents reported ceding substantial authority over what and how much their children eat. Clinicians will recognize this dynamic: when every meal risks a meltdown, allowing the child to dictate the menu can feel like the only sustainable path, even as it narrows the diet further and shifts the household’s nutritional baseline.

The statistical heart of the paper lies in its correlation analyses, which connect the child’s feeding profile to the parent’s lived experience. The BAMBI and STEP total scores were significantly and positively correlated, with a p-value of 0.009, meaning that children rated as showing more problematic mealtime behaviors were also flagged for more feeding problems on an independent instrument, a convergence that strengthens confidence in the measurement. More consequential for family well-being, the BAMBI total score was negatively correlated with the QoLA Part A score, which reflects the parent’s perception of the child’s quality of life, at p = 0.039. In other words, the more severe the mealtime behavior problems, the poorer parents rated their child’s overall quality of life, positioning feeding not as an isolated domain but as a lens through which families judge broader functioning.

The second key association ran in the opposite emotional direction. The STEP total score was positively associated with the QoLA Part B score, which measures parental quality of life and the burden parents experience, at p = 0.004. Because higher Part B scores on the QoLA correspond to greater difficulty and lower parental well-being, this finding indicates that more severe feeding problems were linked to worse caregiver quality of life. The mean QoLA scores in the sample, 91.0 with a standard deviation of 16.53 for Part A and 59.88 with a standard deviation of 19.27 for Part B, provide context for where these families sit on both dimensions. Together, the two correlations sketch a bidirectional picture: feeding problems color how parents see their child’s life, and they also erode the parents’ own well-being, a pattern consistent with prior research showing that mealtime stress is a significant contributor to caregiver strain in autism households.

The nutritional implications of such restricted eating are not trivial. The wider literature cited by the authors, including meta-analyses comparing dietary intake between children with autism and typically developing peers, documents inadequate intakes of several micronutrients, and scoping reviews have catalogued deficiencies in vitamins and minerals among selectively eating autistic children. In severe cases, case reports and case series have described nutritional deficiency diseases arising from avoidant and restrictive eating patterns associated with autism. The one-third of this sample classified as overweight or obese adds a second, seemingly contradictory risk layer, since energy-dense selective diets can simultaneously deliver excess calories and insufficient micronutrients. This double burden, overenergy alongside undernutrition, is increasingly recognized as a signature nutritional challenge in pediatric autism and underscores why routine growth monitoring and dietary assessment belong in standard care pathways.

The authors conclude that food selectivity and feeding problems are common in children and adolescents with autism spectrum disorder and are associated with lower parental quality of life for both the affected individuals and their caregivers, a conclusion they argue highlights the need for early, family-centered, multidisciplinary nutritional interventions. The emphasis on family-centered care is significant. Because the study found parents deferring to child control of intake, interventions that target the child alone may miss the relational dynamics that sustain restrictive eating. Multidisciplinary teams that combine dietitians, behavioral therapists, occupational therapists addressing sensory sensitivities, and clinicians monitoring gastrointestinal health are better positioned to broaden diets gradually while reducing the mealtime conflict that wears parents down.

As with all cross-sectional designs, the study captures associations rather than causal pathways, and its modest sample of 48 children means the findings are best read as a carefully measured snapshot rather than a definitive epidemiological estimate. Even so, the convergence of independent instruments, the alignment with international meta-analytic evidence on obesity and food selectivity in autism, and the explicit linkage between child feeding severity and caregiver quality of life give the results practical weight. For clinicians, the message is to screen for feeding problems early and treat them as a legitimate clinical target rather than a behavioral footnote. For parents exhausted by the nightly standoff over a single accepted brand of pasta, the study offers validation: the struggle at the table is real, measurable, and, importantly, treatable with the right support.

Subject of Research: Feeding problems, nutritional status, and parental quality of life in children and adolescents with autism spectrum disorder

Article Title: Beyond Food Selectivity: Feeding Problems, Nutritional Status, and Parental Quality of Life in Autism Spectrum Disorder

Article References: Kılınç Tulum, G. E., Açar Kuru, Y., & Özenoğlu, A. (2026). Beyond Food Selectivity: Feeding Problems, Nutritional Status, and Parental Quality of Life in Autism Spectrum Disorder. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-026-07558-z

Image Credits: AI Generated

DOI: 10.1007/s10803-026-07558-z

Keywords: autism spectrum disorder, food selectivity, feeding problems, nutritional status, parental quality of life, mealtime behavior, pediatric nutrition, overweight and obesity, BAMBI, STEP, QoLA, family-centered intervention

The research involved 171 school-aged children drawn from five primary schools in Spain, assessed between January and March 2025. The design was deliberately comprehensive. Each child underwent standardized anthropometric assessment following the International Standards for Anthropometric Assessment, including weight, height, and calculation of BMI-for-age z-scores referenced against World Health Organization growth data and CDC growth charts. Blood pressure was measured and interpreted according to the fourth report of the National High Blood Pressure Education Program Working Group, the reference standard for pediatric hypertension screening. Dietary quality was captured with the KIDMED index, a sixteen-point instrument developed by Serra-Majem and colleagues that scores adherence to the Mediterranean dietary pattern, long considered the gold-standard model of healthy eating in the region. Parents and children also reported dietary intake, which the researchers converted into percentages of recommended energy and nutrient targets using the Spanish food composition database BEDCA and European Food Safety Authority dietary reference values.

On the headline question, the results were mixed but informative. Children attending the school cafeteria scored a mean of 5.55 points on the KIDMED Mediterranean diet index, compared with 4.80 among non-attendees, with standard deviations of 3.32 and 3.69 respectively. That difference, while directionally favorable to the cafeteria group, was modest in absolute terms and sits against a sobering backdrop: both scores fall well below what would indicate strong adherence to a dietary pattern associated with lower cardiovascular and metabolic risk. Recent meta-analyses of Spanish youth have documented a worrying decline in Mediterranean diet adherence, and this study’s numbers, hovering near the middle of the scale, are consistent with that national trend. In other words, the cafeteria may nudge children in the right direction, but neither group of children is eating particularly well by the standards of the traditional Mediterranean pattern.

Perhaps the most counterintuitive finding concerns what the children who ate at home were actually consuming. Non-attendees reported higher energy intake, at 108.3 percent of recommended levels compared with 94.4 percent for cafeteria users, and markedly higher lipid intake, at 128.5 percent of recommendations versus 105.9 percent, a difference that reached statistical significance with a p-value of 0.006. The home-fed group also reported higher intakes of several micronutrients, with differences significant at p below 0.05. The authors interpret this pattern as suggestive of a more energy-dense dietary profile among children eating at home, one that delivers abundant calories and fat alongside certain micronutrients. That combination is precisely the signature epidemiologists associate with the modern obesogenic diet: plentiful energy and fat, often from processed and palatable foods, rather than the balanced, lower-energy-density pattern the Mediterranean model prescribes.

Yet when the researchers turned to the physical measurements, the expected differences largely failed to materialize. BMI-for-age z-scores and blood pressure did not differ significantly between cafeteria attendees and non-attendees, with p-values above 0.05. The one anthropometric exception was weight percentile, which did differ significantly between the groups at p below 0.001, though the cross-sectional design of the study makes it impossible to determine whether cafeteria attendance influenced weight, whether families with children of different weights choose cafeteria use differently, or whether some third factor, such as household income or parental work schedules, drives both. This is the fundamental limitation of cross-sectional evidence: it captures a snapshot, not a causal pathway, and self-selection into school meal programs is well documented in the international literature.

Where the study truly broke new ground was in its analysis of the school menus themselves, and specifically their cost. The researchers assessed the nutritional composition of the menus on offer and the daily cost per child, then tested whether these variables related to children’s intake and health indicators. The results were emphatic. Menu cost and school type, whether public, state-funded private, or fully private, were significantly associated with nutrient intake, anthropometric percentiles, and Mediterranean diet adherence, all at p below 0.001. In plain terms, the price tag attached to a school’s lunch program appeared to travel through the food on the plate and into the bodies and health markers of the children eating it. This is a socioeconomic gradient rendered in nutrients and percentiles, and it echoes a growing body of European and global evidence that school food quality varies systematically with the resources behind it.

The implications of that finding reach well beyond Spain. A 2026 review of school meal quality across twenty-nine countries published in Public Health Nutrition documented wide international variation in what schools actually serve, and a 2025 scoping review in Obesity Reviews found that affordability was a recurring fault line in European school food policy. Spain itself has made childhood obesity a national priority, with a strategic plan running from 2022 to 2030 and the long-standing NAOS strategy on nutrition, physical activity, and obesity prevention. The ALADINO 2023 national survey continues to document substantial rates of childhood overweight and obesity, with socioeconomic status among the strongest predictors. Against that backdrop, the Cordoba team’s suggestion that minimum per-child spending on school menus might need to be standardized is a policy proposal with real teeth, because it targets the resource gap that their data suggest is shaping what children eat.

The international evidence on school meals more broadly supports the idea that these programs can be powerful equity tools, particularly for food-insecure children. Systematic reviews of universal free school meal policies in the United States, including a 2024 review in JAMA Network Open and a 2021 synthesis in Nutrients, have found benefits for diet quality, food security, and school participation, with more limited and inconsistent effects on body mass index. Studies of the Healthy, Hunger-Free Kids Act published in JAMA in 2020 showed measurable improvements in dietary quality among participants in the US National School Lunch Program. Reviews spanning low- and middle-income countries have documented benefits for both educational and health outcomes. The Spanish study fits neatly into this literature: it finds dietary signals consistent with benefit, no dramatic anthropometric effects, and a strong suggestion that the quality of what is served, not merely whether it is served, determines the outcome.

Several caveats deserve emphasis before the findings are translated into policy. The sample of 171 children from five schools is small and geographically concentrated, and the cross-sectional design cannot establish that cafeteria meals caused the observed differences in diet quality or weight percentiles. Dietary intake data relied on reported consumption, a method inherently vulnerable to recall and social desirability bias, particularly in children. The KIDMED index, while widely used, is a screening tool rather than a full dietary assessment. And the significant differences in micronutrient intakes favoring the home-fed group complicate any simple narrative that cafeteria eating is uniformly healthier; the pattern more likely reflects overall dietary volume and composition than the cafeteria meal itself. Longitudinal and intervention studies, ideally with objective dietary measures and sufficient power to detect changes in growth trajectories, would be needed to confirm causal pathways.

Even with those limitations, the study lands at a consequential moment. Global analyses from the NCD Risk Factor Collaboration published in The Lancet in 2024 documented that more than 370 million children and adolescents worldwide now live with obesity, a figure that has risen relentlessly since 1990, and pediatric obesity is firmly linked to long-term cardiovascular, metabolic, and psychological consequences. School food environments remain one of the few settings where nearly all children, regardless of family circumstances, can be reached daily. The Spanish findings refine the message for policymakers: simply having a cafeteria is not enough, and the modest dietary advantage observed among attendees suggests that well-designed school meals can gently steer children toward better patterns. But the strong associations between menu cost, school type, and nearly every nutritional outcome measured indicate that the deeper variable is investment. If school meals are to function as an equity instrument, the authors’ case for guaranteeing a minimum standard of per-child spending may prove to be the study’s most important contribution.

Subject of Research: Associations between school cafeteria attendance, dietary intake, and nutritional status in Spanish primary school children

Article Title: Associations between school meal attendance, dietary intake and nutritional status in Spanish school-aged children: a cross-sectional study

Article References: Uclés-Torrente, M. D. M., Ruiz-Gamarra, G. E., Alcalde-Llergo, J. M., Vaquero-Álvarez, M., Blancas-Sánchez, I. M., Aparicio-Martínez, P., & Vaquero Abellán, M. (2026). Associations between school meal attendance, dietary intake and nutritional status in Spanish school-aged children: a cross-sectional study. Pediatric Research. https://doi.org/10.1038/s41390-026-05442-1

Image Credits: AI Generated

DOI: 10.1038/s41390-026-05442-1

Keywords: school meals, childhood obesity, Mediterranean diet, pediatric nutrition, school cafeteria, dietary intake, nutritional status, menu cost, socioeconomic gradient, KIDMED index, Spain, public health

The research, published in Annals of Gastroenterological Surgery, drew on the Setouchi Colorectal Neoplasm Registration database, which aggregates data from 15 hospitals affiliated with Okayama University. Between January 2011 and December 2022, the database identified 402 cases of colorectal cancer in patients aged 90 or above. After excluding patients lacking the nutritional measurements needed for analysis, 225 nonagenarians who underwent primary tumor resection remained in the cohort. The median age was 92 years, two-thirds were women, and nearly half carried an American Society of Anesthesiologists classification of 3, indicating severe systemic disease. This is, by any standard, a fragile population facing major surgery.

The tool at the center of the study is the Geriatric Nutritional Risk Index, or GNRI. It is calculated from serum albumin levels and body mass index using a straightforward formula: 14.89 times the albumin concentration in grams per deciliter, plus 41.7 times the BMI divided by 22. Albumin, the most abundant protein in blood plasma, reflects both nutritional intake and systemic inflammation, while BMI captures energy reserves. The index was originally developed to grade nutritional risk in hospitalized older adults and has since been validated as a prognostic marker across a range of surgical settings, including colorectal cancer resection in older patients. Its appeal lies in its simplicity: no specialized equipment, no subjective scoring, just two routinely collected values combined in seconds.

The researchers stratified their cohort using two cutoff values. The first, 98, comes from the existing literature and served as the prespecified threshold for the primary analysis. The second, 86, emerged from a post hoc receiver operating characteristic analysis of the cohort itself, identifying the value that best discriminated vital status at three years, with a sensitivity of 0.62 and a specificity of 0.55. By the literature-based cutoff, only 36 of the 225 patients qualified as nutritionally well; by the cohort-derived cutoff, the groups split almost evenly. The median GNRI across the whole cohort was 85.9, and median serum albumin was just 3.2 grams per deciliter, figures that underscore how prevalent malnutrition is in this age group even before cancer enters the picture.

On the short-term outcomes, the findings were nuanced. Postoperative complications of any severity, graded according to the Clavien-Dindo classification, occurred in 36.4 percent of patients, while severe complications of Grade III or higher affected only 5.8 percent. Thirty-day mortality was a strikingly low 0.9 percent, and only one patient required reoperation. After the investigators applied stabilized inverse-probability-of-treatment weighting, a propensity-score method that balances baseline characteristics such as age, sex, ASA class, pathological stage, and surgical approach between the GNRI groups, there was no significant difference in overall complication rates between nutritionally high and low patients at either cutoff. However, at the cutoff of 98, well-nourished patients did have a significantly shorter expected hospital stay, with a length-of-stay ratio of 0.70, meaning their stays were roughly 30 percent shorter than those of their nutritionally depleted counterparts.

The surgical patterns themselves revealed something important. Patients with high GNRI scores were significantly more likely to undergo laparoscopic rather than open surgery, had longer operative times, and required stoma creation far less often. The authors interpret this as clinical selection at work: frail, malnourished patients tend to have more comorbidities and more complex disease, prompting surgeons to favor open procedures perceived as safer, and to create protective stomas more liberally. This imbalance likely confounded short-term measures such as blood loss, which was significantly lower in the high-GNRI group at the 86 cutoff, and the researchers acknowledge that residual confounding cannot be excluded for these perioperative endpoints despite the weighting adjustment.

It is the long-term survival data that carry the most weight. In the weighted Cox proportional hazards analysis using the cohort-derived cutoff of 86, patients with high GNRI had a hazard ratio for death of 0.54 compared with low-GNRI patients, a 46 percent reduction in the risk of dying that was statistically significant. Median survival was 1,283 days in the high-GNRI group versus 700 days in the low-GNRI group, a difference of more than a year and a half. At the literature-based cutoff of 98, the same direction of effect was seen but did not reach significance in the full cohort, with a hazard ratio of 0.67. However, in a sensitivity analysis restricted to the 194 patients without metastatic disease, the adjusted hazard ratio at the 98 cutoff fell to 0.52 and became statistically significant, suggesting the survival advantage of good nutritional status is not merely an artifact of including patients with stage IV cancer.

Perhaps the most arresting detail lies in the causes of death. Of the 100 patients who died during follow-up, 18 deaths were definitively attributed to colorectal cancer, 33 to other causes, and 48 remained undetermined, a reflection of the practical difficulties of tracking very old patients who move in with family or enter nursing facilities. Yet every single one of the 18 confirmed cancer-related deaths occurred in patients with a GNRI below 98, and not one occurred in the nutritionally robust group. The authors caution that the large proportion of undetermined deaths makes cancer-specific survival analysis unreliable, but the pattern is difficult to dismiss entirely and hints that malnutrition and cancer lethality may travel together in this age group.

The mechanisms linking low GNRI to poor outcomes are biologically plausible. Malnutrition impairs wound healing, suppresses immune function, and increases susceptibility to infection, raising the risk of surgical site infections, anastomotic leakage, and pneumonia. It also drives muscle wasting and frailty, eroding the functional capacity that nonagenarians need to recover from a laparotomy or colectomy. Low albumin may additionally signal chronic systemic inflammation, which is independently associated with adverse surgical outcomes and tumor progression. In this framing, the GNRI is not just a number but a composite snapshot of the physiological terrain on which both the surgery and the cancer will play out.

The study has honest limitations. Its retrospective design and complete-case analysis excluded 177 of 402 eligible patients, and the excluded patients differed significantly in ASA class, surgical history, and cancer stage, so the analyzed cohort may not fully represent the source population. The GNRI itself cannot capture micronutrient deficiencies or inflammatory markers, and richer tools such as the Subjective Global Assessment or Comprehensive Geriatric Assessment might add depth. The cohort-derived cutoff of 86 showed only modest discriminatory ability, with an area under the curve of 0.593, and the authors explicitly label it exploratory and in need of external validation. Still, the clinical message is clear and actionable: every nonagenarian being considered for colorectal cancer surgery should undergo GNRI screening, and those scoring low should be referred for preoperative nutritional optimization, whether through dietary counseling, supplementation, or other interventions. As the world’s population ages and the oldest-old increasingly present with operable cancers, a two-variable formula that costs almost nothing to compute may become one of the most consequential numbers on the preoperative chart.

Subject of Research: Preoperative nutritional risk assessment using the Geriatric Nutritional Risk Index in nonagenarians undergoing colorectal cancer surgery

Article Title: The Geriatric Nutritional Risk Index Predicts Short‐ and Long‐Term Outcomes in the Oldest‐Old With Colorectal Cancer: A Multi‐Institutional Analysis of 225 Nonagenarians

Article References: Toshima, T., Teraishi, F., Takanaga, S., Mitsuhashi, T., Inada, R., Ohtani, T., Yoshida, R., Hori, N., Shigemitsu, K., Yamamoto, S., Kubota, T., Okano, Y., Nobuhisa, T., Taniguchi, F., Ishikawa, W., Shoji, R., Matsuda, T., Umeoka, T., Fujiwara, T., & The Setouchi Colorectal Neoplasm Registration Study Group Collaborators (2026). The Geriatric Nutritional Risk Index Predicts Short‐ and Long‐Term Outcomes in the Oldest‐Old With Colorectal Cancer: A Multi‐Institutional Analysis of 225 Nonagenarians. Annals of Gastroenterological Surgery, Article ags3.70288. https://doi.org/10.1002/ags3.70288

Image Credits: AI Generated

DOI: 10.1002/ags3.70288

Keywords: colorectal cancer, nonagenarians, Geriatric Nutritional Risk Index, nutritional status, surgical outcomes, overall survival, serum albumin, frailty, elderly surgery, propensity score analysis, Clavien-Dindo complications, preoperative assessment