Nephritis

Using the NHANES training data, we applied a Cox penalized regression model—where the hazard of aging-related mortality (mortality from diseases of the heart, malignant neoplasms, chronic lower respiratory disease, cerebrovascular disease, Alzheimer’s disease, Diabetes mellitus, nephritis, nephrotic syndrome, and nephrosis) was regressed on forty-two clinical markers and chronological age to select variables for inclusion in our phenotypic age score. Ten-fold cross-validation was employed to select the parameter value, lambda, for the penalized regression. In order to develop a sparse parsimonious age estimator (fewer biomarker variables preferred to produce robust results) we selected a lambda of 0.0192, which represented a one standard deviation increase over the lambda with minimum mean-squared error during cross-validation (Supplement 1: Fig. S13). Of the forty-two biomarkers included in the penalized Cox regression model, this resulted in ten variables (including chronological age) that were selected for the phenotypic age predictor.
These nine biomarkers and chronological age were then included in a parametric proportional hazards model based on the Gompertz distribution. Based on this model, we estimated the 10-year (120 months) mortality risk of the j-the individual. Next, the mortality score was converted into units of years (Supplement 1). The resulting phenotypic age estimate was regressed on DNA methylation data using an elastic net regression analysis. The penalization parameter was chosen to minimize the cross validated mean square error rate (Supplement 1: Fig. S14), which resulted in 513 CpGs.

The California Office of Statewide Health Planning and Development (Sacramento, CA) provided patient discharge data and ED visit files. Diagnoses in both sources are coded according to the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM; Centers for Disease Control and Prevention 1979 ). Of the hospital admissions during this period, 41% occurred among those people who had been admitted from an initial ED visit. The ED data, on the other hand, included all ED visits, regardless of whether patients were subsequently admitted to the hospital (of which 16% were). Thus, our resulting data sets for all incidents of ED visits and inpatient hospitalizations were not mutually exclusive.
Heat waves exacerbate a wide range of preexisting illnesses, and excess morbidity and mortality associated with heat waves go beyond that classified formally as “heat related.” With this in mind, we classified morbidity outcome data into categories consistent with the major groups applied in previous heat wave hospitalization studies (notably Semenza et al. 1999 (link)) and included all diagnoses combined, all internal causes (ICD-9-CM codes 001–799.9), diabetes mellitus (250), disorders of fluid and electrolyte balance (276), cardiovascular diseases (390–398, 402, 404–429, 440–448), acute myocardial infarction (MI; 410), cerebrovascular diseases (430–438), respiratory illnesses (460–519), nephritis and nephrotic syndrome and nephrosis (580–589), acute renal failure (584), and heat-related effects (992).
Because risk appears to be greatest in very young and old persons, we used three age categories in the analysis: 0–4 years, 5–64 years, and ≥ 65 years of age. We used several race and ethnicity categories that have been previously applied in health vulnerability analyses in California: Asian/Pacific Islander, African American, Latino/Hispanic, Native American/Alaska Native, other, unreported race/ethnicity, and non-Hispanic white.
Some studies of heat wave–related hospitalizations have found that using primary discharge diagnoses alone can underestimate increases in some admissions (Kilbourne 1999 (link); Semenza et al. 1999 (link)). To calculate rate ratios (RRs) among ED visits and hospitalizations, we combined the primary and the first nine secondary diagnoses listed in the discharge record; for example, we classified admissions that included a nephritis code, regardless of whether primary or secondary, as nephritis in this analysis. Because there may be appreciable variation in the order of the various cause codes in the primary (the condition that prompted the admission or visit, not necessarily the most severe condition) and secondary diagnoses, we combined the primary and the first nine secondary codes to lend more consistency to the heat-wave versus non-heat-wave descriptive epidemiology.
To estimate the number of excess hospital admissions and ED visits during the heat wave and describe the principal disease process treated, we evaluated primary discharge diagnoses separately to tabulate changes in the total numbers of individuals seeking treatment.

Cryostat sections prepared from 4% paraformaldehyde-perfused control or MOG35-55-induced EAE female mice were subjected to heat-induced epitope retrieval (HIER) and incubated overnight at 4°C with anti-e1 serum and rabbit anti-Cter Kir4.1_356-375 antibody, revealed by AF594-coupled and AF488-coupled secondary antibodies, respectively. Sections were stained with DAPI and coverslipped with anti-fading mounting medium, and pictures were taken at fixed fluorescence exposure. Peptide-N-glycosidase F (PNGase F, New England BioLabs) was used to evaluate the effect of N-linked glycosylation on the anti-e1 reactivities. For this, HIER-treated sections were incubated with 5 U/µl of PNGase F in 10 mM PBS, 10 mM EDTA at pH 7.6 at 37°C overnight, before being processed for immunohistofluorescence.
For fresh-frozen human tissues, 12µm-thick cryostat sections enriched in subcortical WM (Supplementary Table 3) were prepared from selected blocks containing inflamed subcortical WM or NAWM,^{35 (link)} defined from CD68 and Luxol Fast Blue stainings (Supplementary Table 4). Acetone-fixed sections were processed for Kir4.1 immunostaining as described above, and incubated with 0.1% Black Soudan to stain the white matter and hide lipofuscin-driven autofluorescence before covering with anti-fade mounting medium. For quantification, three fields at ×40 objective of subcortical WM per sample were acquired at fixed fluorescence exposure time, and the average level of Kir4.1 immunofluorescence was measured with ImageJ software. Sections from four human renal fresh-frozen biopsies were also used for Kir4.1 immunofluorescence: control cortical pre-implant biopsies from two donors and cortical kidney biopsies with chronic inflammation from two patients with interstitial fibrosis/tubular atrophy.